Strategic insight and guidance to assist higher education institutions in leveraging the benefits of technology&nbsp; – from artificial intelligence to campus infrastructure

Digital transformation

This is MAIN TEXT , added image here.
  
      
  
    Image
                



          

  

  
      
  
    Image
                



          

  
&nbsp;
  
      
  
    Image
                



          

  


test-Article

It's a test article.-1

Smoke test Article - 1

This is the main text ,now adding image to see f in Front end this image is as broad as the text of the article.Adding another line to check how this will look in the front end.
  
      
  
    Image
                



          

  
ABOVE &nbsp;IS SMALL IMAGE . &nbsp;BELOW IS FIT TO TEXT SIZE IMAGE.
  
      
  
    Image
                



          

  
&nbsp;
  
      
  
    Image
                



          

  
&nbsp;
  
      
  
    Image
                



          

  
&nbsp;Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.

Just a test to if this resource is added on top of the list

1st Test Resource

Test - Test - Test

Test—this--Article1st

Test

AAA Test 439 – check changed

Testing here

Test New for En Dash

Continuous e-assessment is an effective tool for engaging students with online materials. Not only that, but online tests also allow students to learn in smaller chunks, which reduces procrastination and the need to study intensively just before exams. To solve these tests, students must engage with course materials, enhancing their understanding and identifying areas for further focus. E-assessments have been shown to significantly increase activity within the virtual learning environment (VLE). Instant individual feedback also helps students prepare better for final assessment.Setting up online tests requires an investment of time and effort to ensure alignment with learning outcomes, and you need to run them with clear instructions and rules to avoid any confusion with students. Nevertheless, educators also reap benefits from online tests, such as automated marking and ease of reuse.Spotlight collection: what does good assessment look like online?Read more: How to use peer feedback to build engagement online10 tips for instigating an assessment transformation programmeAs an educator teaching core and compulsory modules in economics at the University of Southampton, I&nbsp;strive to find innovative ways to engage my students. To pilot this approach, in my modules I&nbsp;introduced low-stakes continuous e-assessment via our VLE.This new assessment method proved to be helpful for students because they engaged more with online materials and for me to understand aspects of the module that may need further clarification.Designing an online test in a virtual learning environmentEnsuring accessibility and usability of online tests is crucial. Tests should be designed to accommodate diverse learners, including students with disabilities, and incorporate features such as screen reader compatibility and adjustable time settings. A good starting point is the web content accessibility guidelines (WCAG).The instructor should take the test before launching it to fix any technical issues and ensure that questions are clear. Are the instructions easy to understand? Is the test easy find in the VLE and navigate? Do students know what to do if they have technical difficulties?Implementing an online test&nbsp;My goal with continuous e-assessments is to engage students with online materials, and for each student to gain a good understanding about their strengths and weaknesses, improving their academic performance.I have tried different ways of implementing online tests. From my experience, I&nbsp;use weekly timed tests, with a two-week submission window. In that way, students have less pressure and more flexibility to learn the materials needed for each test.Summative assessments have worked better than formative assessments on engagement and performance, whether that’s quiz format or other forms of low-stakes assessment. Typically in my modules, I&nbsp;have 10&nbsp;online tests count for 20&nbsp;per cent of the total mark and the rest is group assignments and the final exam.Students have two attempts in each test (each attempt has slightly different questions in a different sequence). This makes students feel less pressured when they take the test. Only the revision test allows just attempt to prepare them better for final exam. I&nbsp;release each test’s answers and individual feedback after the submission deadline of each test.What about academic integrity concerns?Adopting continuous e-assessment raises concerns about academic integrity. Risks include students googling answers or collaborating dishonestly. However, educators can use strategies to minimise risk of academic misconduct: keep stakes low and craft questions carefully.Each online test counts for only 2&nbsp;per cent of the final mark, which reduces the incentive to cheat. A careful design of questions can discourage straightforward searches for answers. Educators can test questions in tools such as ChatGPT to ensure that they cannot be easily answered through a simple online search.Although collusion is a concern, I&nbsp;see it as less significant in low-stakes settings. Collaborative engagement with materials can enhance learning.To further mitigate collusion, I&nbsp;use a question bank to provide different sets of questions for each student. In multiple-choice questions, I&nbsp;include slight variations, while features such as calculated formula questions in the VLE adjust values for each student.Are students engaging with the online materials?Feedback from students, gathered through our student-staff liaison committee and module feedback surveys, shows strong preference for low-stakes continuous assessments. Students appreciate opportunities to test their knowledge and receive instant feedback about their understanding of the materials taught.Nearly 90&nbsp;per cent of our first-year students engaged with online materials through weekly tests, and there was positive and significant correlation between test completion and performance on final exam, as we found for the period 2020-24 (see Additional Links).Final verdict on continuous e-assessmentStudents’ feedback on the pilot study was excellent, and I decided to keep online tests as a method of assessment and engagement. My modules have been nominated and shortlisted for Blackboard and VLE awards, often highlighting the extensive use of continuous e-assessment. My experience with weekly online tests has been overwhelmingly positive. I&nbsp;encourage other educators, particularly in quantitative subjects, to consider integrating e-assessments. Benefits for both students and educators are significant and can enhance overall learning experience.Panagiotis Giannarakis is a senior teaching fellow in the department of economics in the School of Economic, Social and Political Sciences at the University of Southampton.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Continuous e-assessment helps students engage with course materials and reduce procrastination, and offers educators benefits such as automated marking

Online tests as a method of engagement in higher education

The demand for flexible, accessible learning options has never been greater, but as online learning sees continued growth globally, how can institutions stand out in a crowded marketplace? This is a huge challenge we attempt to tackle at the University of Edinburgh, as we offer a massive portfolio of more than 80 online postgraduate degrees and more than 100 massive open online courses (Moocs). In this article, we’ll explore practical marketing tips and strategies to help you navigate the online education space, boost enrolment numbers and ensure your programmes get the visibility they deserve.Email marketing is keyMaster’s: one of the main goals for our online master’s marketing is to get prospects to sign up for our mailing list. They will then receive regular emails containing content such as the benefits of doing an online master’s, how to apply, student features and so on. We compare open rates and link clicks to industry averages and measure how many subscribers we have successfully converted to applicants.Moocs: reaching prospective and existing learners through email is a cornerstone of our marketing strategy. With a wide range of subject areas in our Mooc portfolio, there is ample opportunity to send updates en masse or segment readers to funnel them towards courses related to their interests and keep them engaged with other relevant learning opportunities or upcoming online learning events.Invest in paid advertisingMaster’s: the bulk of our online master’s marketing budget is spent on our annual university-wide ad campaign. Every online programme contributes a set amount towards the budget, and we work with an external media agency to identify the best channels. We’ve seen great results from the campaigns, and they have helped us hold our own in an increasingly competitive marketplace.Moocs: most of our Moocs are produced and marketed on a limited budget. Usually, this means most of our marketing efforts tend to fall on the organic side of the spectrum. However, when there is budget, crafting strategically placed ads on Google search and social channels such as LinkedIn, Instagram and YouTube has led to significant improvements in content engagement and enrolment rates when compared with organic-only (non-paid-for) campaigns. The most effective adverts are ones that resonate with the audience, and have memorable visuals and a strong call to action.Resource collection: Creating online learning communitiesCreating a welcoming and inclusive online learning communityWell-being pedagogies: activities and practices to improve the student experience onlineFeature your student testimonialsMaster’s: The authentic student voice provides value to our online master’s marketing activities. Whether it’s students explaining why they chose to study online, advising new students, talking about the benefits and challenges of remote learning, praising how their degree has enhanced their career prospects or filming a desk tour video...people love to hear directly from them! Prospective students also have the option of chatting online with current students through our student ambassador scheme.Moocs: There’s an overwhelming number of Moocs available online – with options for those looking to learn for fun, and those looking to upgrade their professional skills. Learner testimonials are key to helping those on the fence see where the course can take them, and what they can expect to learn. The days of raving about how good your course is are over. Highlighting the experiences of other learners helps provide social proof, validation and intrigue – a feat that simply can’t be replicated by self-promotion alone.Generate content for organic social mediaMaster’s: Content on organic social media channels (meaning non-paid social posts) can increase brand awareness and lead to conversion. Inspiring and insightful interviews from graduating students always gain great engagement. Other than that, we regularly post student-generated vlogs or blogs, tips on studying online, resources for online students and the profiles of our student ambassadors. Our highest-performing Instagram reel is just a simple motivational quote overlaid on top of a seven-second video of Edinburgh Castle.Moocs: Entertaining and educational content is what your learners want to see on socials. Without fail, videos under 60 seconds have stood out as the consistent winners in our social media reviews. There is a big opportunity here to further develop relationships with your audience and subtly show the benefits of the courses you’re promoting. Giveaways can also be a great option to boost your metrics on socials organically. Earlier this year, one of our giveaway competitions resulted in our increasing our followers by 14 per cent, as well as gaining more learner testimonials in the process. The giveaways were targeted at people with an interest in short online courses, including our own social media followers and those who followed our Mooc partners. Our followers were quite motivated to participate because the prize was free access to a course of their choice.Engage your audience through events and commsMaster’s: our postgraduate online learning open days is our flagship event that takes place over two days every May. Comprising more than 100 live webinars, attendees can hear from and ask staff and students questions about online learning. Furthermore, we have smaller virtual open days to keep prospects engaged throughout the year.Moocs: Also included in our open days are sessions focused on our portfolio of short online courses. We find attendees are keen to discover all our online learning options and taking a short online course may persuade them to progress to an online master’s in the future. As mentioned earlier in this article, email newsletters also serve as a reliable method for keeping new and returning learners informed. Lastly, and more rarely, hosting Mooc launch events can be a brilliant way to engage with your prospective learners in person, enabling you to better understand their approaches to learning and gain rapid feedback.Promoting online learning can and will look slightly different for everyone. Here’s a quick recap of some things to keep in mind when refining your strategy:Focus on improving your email communication with your audienceExperiment with and analyse the results of paid advertising on relevant platformsHighlight the student voice and experiences in your content strategySpend more time creating content that your audience wants to seeCreate opportunities to engage and create dialogue with prospective learners.Luke Howard is a digital engagement officer in the online learning marketing team at the&nbsp;University of Edinburgh. Ari Badlishah is digital engagement officer in the online learning marketing team at the University of Edinburgh.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Promoting online courses: five marketing strategies 

Several ways to promote Moocs and other online courses, including a focus on email marketing and paid advertising 

Promoting online courses: five effective marketing strategies

Online learning is no longer considered a “disruptor”. It is now a credible and popular alternative to traditional face-to-face learning. Learners need, and want, the flexibility that online learning can provide. At the University of Canterbury (UC), we have seen exponential growth in the demand for online learning across a range of short courses, microcredentials and longer programmes.From school-leavers to mid-career upskillers and lifelong learners, learners are choosing to study online because it suits their busy lives, letting them choose when and where to engage. Online education can offer those with no other options the opportunity for further study. Consider the story of Zakkaia Waipouri, a student who faced not only health restrictions but also the physical barrier of the Southern Alps of Aotearoa New Zealand when considering higher education. Accessible and flexible study through UC&nbsp;Online enabled her to successfully complete a UC&nbsp;qualification.Online learning also brings key benefits to industry and government; it caters to a range of diverse learning needs and styles, provides industry-relevant skills development to teams in an efficient delivery method, and offers access to world-leading research and knowledge from a university without the cost and inconvenience of sending employees to a physical campus. Graduates come into the workforce armed with great knowledge in their field but will need to upskill throughout their careers, and online options can fill that need. A provider that is backed or accredited by a reputable institution such as a university should always be the provider of choice.Spotlight collection: lifelong learning that will lastTHE podcast: microcredentials are knocking. Will higher education answer?What about lifelong learning for international students?So, the value of truly accessible education is recognised, but what exactly are learners paying for when they sign up to an online higher education course or programme of study?Value for money can be subjective, but there are a few factors that learners are generally looking for in an online programme or course, and these determine what they will likely be prepared to pay. These drivers include: courses that help them achieve a formal qualification to practise or progress in their career; courses that help them keep pace as skills requirements evolve; and courses that help them make career shifts.When designing and developing programmes or short courses, it is important to know the learner that you are targeting. Personas are a useful tool to use to profile your target learners before designing the course.Managing cost of online course design and deliveryCovid-19 accelerated the popularity of online learning (through necessity), but it also contributed to a negative perception of the model. Poorly designed courses with little to no learner engagement proliferated. The concept of “recorded lectures” persists, along with the assumption that online learning must be a quick, cheap option for a provider. In reality, high costs of both course creation and delivery, combined with an intense and time-consuming process, mean that online learning is not at all cheap.The time it takes to create a course is a major cost factor. Understanding how the course content can be most engaging and delivered in the best way for the topic and learner cohort should not be rushed. A highly skilled educational media and design team and adhering to strong quality assurance contributes to a lengthy process.However, efficiency and cost-saving practices can be employed. Planning programmes and courses in advance allows for overlaps in material to be factored into your designs for efficiency purposes. Different programmes – in health, engineering and business, for example – may all include a leadership course. These courses will likely have common elements that can be duplicated or adapted. Courses that are part of a programme may lend themselves to a series of shorter courses. Courses at the beginning of a programme may lend themselves to learners needing introductory-style short courses, whereas courses near the end of a programme may work for those who are already qualified and want to dip into new cutting-edge material.The careful use of AI can also offer efficiencies to course designers. Despite these, the human element will always remain at the heart of online learning design and delivery. Call on the expertise of your researchers and teachers, as well as bringing in industry expertise. However, ensure that you engage your subject matter experts (SMEs) early and negotiate how their time will be leveraged as your access to these often very busy people will probably be limited. It is very important that you have good access to your SME early on to ensure that you emphasise good design, and from there it’s usually a steady flow of communication and information between your SME and learning designer.It is not just content that these experts and skilled facilitators provide; often they add their own flavour into the courses, update content as the course progresses, and contribute time and insights in synchronous sessions for learners.Elements that enrich online learningOnline learning continues to challenge the notion that a learner cannot acquire the same learning community experience as in a face-to-face model. This is where innovative and intense course design and detailed delivery planning come into play. Interactive and responsive forum engagement, opportunities to engage with fellow learners and the course facilitator, and synchronous online meetings can elevate an online course to a new level of engagement for learners.At UC, we define an online course as at least 80&nbsp;per cent online, allowing for face-to-face time when relevant or appropriate, and when it adds real value to the learner. Activities are built to enable learners to connect beyond the content. Communities of practice encourage learners to share ideas and interpretations of the content and to find ways to use their new skills and knowledge. Assessments in online courses are no longer limited to exams, but also include authentic learning opportunities often tied to work-related, real-life outcomes.Online learning has long evolved past the point of recorded talks and continues to innovate to improve the learner experience and outcomes. Flexibility remains the key attraction, but understanding the considerable investment that is made into quality online content should give learners a sense of confidence that their learning and resulting credentials are world-class.Mick Grimley is director and dean, and Lisa Burdes is partnerships manager of UC&nbsp;Online, both in Future Learning and Development at the University of Canterbury.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Understanding the time investment, industry input and subject matter expertise that go into creating quality online content should give learners a sense of confidence, write Mick Grimley and Lisa Burdes

What are students paying for when they learn online?

In a world where algorithms can streamline processes and data can inform decisions, conduct matters more than knowledge or skills. It is the ethical compass, the ability to inspire and the commitment to integrity that truly distinguishes exceptional leaders.&nbsp;In today’s technology-dominated business landscape, nurturing character and fostering a culture of principled leadership is more important than ever.Teach your students to adapt&nbsp;Unstructured learning is a route to teaching skills in creativity, reflection and imagination. We’ve developed it as part of our learning approach, which acknowledges the changing nature of the workplace.&nbsp;Open dialogue about AI in HE is the way forwardAI communities of practice can help us face its challenges head onHow to navigate the grey areas of AI ethicsEmphasising unstructured learning empowers our students to navigate ambiguity, think critically and innovate in ways that transcend traditional boundaries. We achieve this by encouraging students to pursue real-world problems without a defined structure, such as through the Business Impact Challenge. In the full-time programme, students participate in internships or co-op programmes that immerse them in dynamic workplace environments, where they encounter unpredictable challenges.&nbsp;Additionally, through peer-led learning groups, students engage in collaborative discussions that foster creativity and critical thinking as they tackle complex issues together. We want to equip Generation Z with the adaptive mindset and versatile skill set they’ll need to thrive in careers that may not exist yet, including critical thinking, which enables them to analyse complex problems and make informed decisions, particularly in areas such as AI and digital transformation; digital literacy, allowing them to navigate and leverage emerging technologies effectively; and collaborative communication, fostered through sessions on diversity and inclusion and group coaching, which enhance their ability to work in diverse teams and articulate ideas clearly.This holistic approach to education integrates real-world experiences, ethical principles and a wide range of perspectives. Entrepreneurship Track sees students delve into real-world entrepreneurial endeavours, guided by mentorship programs and practical projects. This instills technical skills and ethical decision-making.&nbsp;For example, students can develop business plans for existing start-ups that they have created. They’re stimulated to participate in pitch competitions, with one of the groups this year placing in the top five projects presented at UP TECH and, because they have tutors throughout the programme with relevant experience, including alumni, they receive invaluable guidance and insight that enhances their entrepreneurial journey.&nbsp;Meanwhile, the Leadership Track hones interpersonal skills and ethical leadership qualities for management in the age of AI and technology.Inspire and motivate teamsThrough interactive workshops and guest lectures, MBA students learn how to influence their colleagues while upholding ethical standards. Our learning model emphasises experiential learning and reflection, encouraging students to critically examine their values and behaviours.For example, in the Leadership Track, we run a Leadership World Café, where one of the topics includes discussing Leaders’ Dilemmas. This engaging format allows students to collaboratively explore complex ethical scenarios and reflect on their decision-making processes, fostering a deeper understanding of their values and how they align with effective leadership.&nbsp;Finally, through practical applications embedded in our curriculum, such as consulting projects and simulations, students are challenged to apply ethical leadership principles in real-world scenarios, reinforcing the importance of integrity in business and leadership. Creating learning communities that can operate globally provides an enriched learning experience in executive education. And that is something that the MBA network promotes in a very concrete way.Humanisation holds the key to success&nbsp;In a world driven by AI, standing out from the competition is more important than ever. We strongly believe that focusing on human connections and understanding is essential for achieving success. While AI can standardise many processes, it is the unique human skills – such as empathy, creativity, and critical thinking – that will continue to set individuals and organisations apart.&nbsp;As technology advances, the ability to connect on a human level becomes even more critical. That’s why we place immense value on the campus living experience, the guidance of inspirational faculty, networking opportunities, engaging alumni events and exposure to industry-leading business leaders.&nbsp;While we recognise the importance of hybrid and online models for their flexibility, we continue to create impactful in-person experiences, such as talks with CEOs who share their insights and experiences, team-building activities focused on social impact – such as community service projects that involve painting houses and revitalising local spaces – and alumni events designed to stimulate networking and foster lasting connections. These initiatives allow students to engage with their peers and mentors in meaningful ways.These elements not only enrich the educational journey but cultivate essential soft skills, emotional intelligence and a strong sense of community. In a landscape where AI can streamline processes and data can drive decisions, it is the human touch, the ability to inspire, empathise and connect, that will set individuals and organisations apart.&nbsp;Preparing Gen Z for leadership roles in the AI eraWe need to prepare Generation Z for leadership roles in the AI era by bridging the gap between technology and ethics. Through our holistic approach to education, we equip students with more than just technical skills. We cultivate a strong ethical compass, an adaptable mindset and a global perspective.&nbsp;Our MBAs emerge as ethical leaders who understand the complexities of AI adoption and can navigate them with integrity. We strongly believe that the MBA journey is still a very important way to face all these challenges, providing a solid foundation for ethical leadership and equipping students with the skills needed to thrive in a changing business landscape.Ana Côrte-Real is associate professor, executive MBA director and head of faculty at Porto Business School.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Let’s equip the next generation with an ethical compass

In an AI-focused world, how can MBA programmes shape the students of today into the compassionate leaders of tomorrow? 

Let’s equip the next generation of business leaders with an ethical compass

Online education has revolutionised the way we teach and learn, offering increased flexibility and accessibility. However, to maximise a digital learning environment’s potential, educators must employ effective teaching strategies that engage learners, facilitate understanding and foster a supportive learning community. Here are eight innovative ways to teach coding in an online context.&nbsp;1. MicrolearningMicrolearning involves delivering content in small, manageable chunks, often in the form of short videos, articles or interactive modules. This strategy helps learners retain information more effectively by breaking complex topics into bite-sized segments.&nbsp;A course on Python could be divided into micro-lessons, each focusing on a single concept such as variables, data types or loops. Learners can watch a five-minute video on how to create a for loop, complete an interactive exercise and test their skills with a quiz all within a 10-minute session. This format allows students to fit coding practice into their daily routines, preventing cognitive overload and improving student retention.2. Interactive multimedia and simulations&nbsp;Tools that facilitate interactive multimedia and simulations provide immersive and engaging learning experiences, enabling students to explore concepts hands-on. In a coding context, educators can use tools such as Adobe Captivate and Articulate Storyline to create interactive content such as simulations, quizzes and demonstrations that engage students.&nbsp;Educators can explore platforms like Scratch or Replit and many other similar platforms that provide real-time coding environments where students can experiment and receive immediate feedback. In Replit, you can create coding challenges where learners write and test Python code directly in the browser.&nbsp;3. GamificationGamification incorporates game elements such as point scoring, leaderboards, badges and challenges into the learning process to make activities more engaging and enjoyable. It encourages healthy competition, making learning fun and increasing participation.&nbsp;To gamify an activity, instructors can organise content into sequential levels or stages that students must complete to advance. Each level should build upon the knowledge gained in the previous one, creating a logical progression through the course material. For example, a level one coding course could introduce basic syntax, while subsequent levels might cover loops, functions and object-oriented programming. Each stage would require mastery before moving on to the next. Instructors can set up challenges or competitions, track progress through leaderboards and award badges for specific achievements to keep students motivated. They can also explore platforms such as Codewars or HackerRank to set up coding challenges.5. Personalised learning pathsPersonalised learning paths cater to the individual needs, preferences and learning styles of each student, allowing them to progress at their own pace. Using these paths ensures that students focus on areas they need to improve while advancing quickly through topics they have mastered, providing a customised educational experience.&nbsp;Instructors can use adaptive learning technologies such as Brightspace LeaP, Blackboard Ally, Knewton Alta and Smart Sparrow to analyse student performance and provide customised content to create personalised learning modules and track learner progress through data analytics. For example, if a student struggles with Python’s object-oriented programming, the system provides additional resources, tutorials and practice problems. Regular assessments and feedback within the coding course help with identifying knowledge gaps and suggesting relevant resources.&nbsp;Some learning management systems feature “intelligent agents” that instructors can configure to monitor student engagement and send personalised recommendations or reminders based on individual progress.Resources on teaching and learning in higher educationTransform digital pedagogy with social mediaWhy your students aren’t speaking up in lectures6. Project-based learningProject-based learning encourages students to apply their knowledge to real-world problems. In an online setting, this can involve virtual simulations, working on case studies or collaborative group projects. These activities not only deepen understanding but also help students develop critical thinking, problem-solving and teamwork skills. Instructors can develop case-based learning modules using tools such as PowerPoint, Adobe Captivate or Articulate Storyline. With PowerPoint, instructors can design slides with multimedia content, add hyperlinks or action buttons to encourage participation and use animations to give feedback. In Adobe Captivate or Articulate Storyline, instructors can build more advanced modules by designing branching scenarios, adding interactive layers, and creating quizzes or simulations where students’ choices determine different outcomes. They can then publish the module online through their learning management system.7. Collaborative learning and peer assessmentCollaborative learning emphasises teamwork and peer-to-peer interaction, fostering a sense of community and collective knowledge-building. It also helps students develop critical thinking, communication, and problem-solving skills – all essential for success in the modern workplace.&nbsp;Instructors can set up discussion forums and virtual study groups on learning management systems to facilitate ongoing peer interaction and support. For example, during a coding project, students might use GitHub to work together on a web application, with one person handling front-end design and another managing the back end. They can use discussion forums to share feedback and ideas and conduct peer reviews to assess each other’s code, suggesting improvements.&nbsp;8. AI professors and chatbotsAI-powered virtual professors and chatbots are revolutionising online education by providing personalised, real-time support to learners. AI professors can guide students through complex topics and offer customised learning paths based on individual progress.&nbsp;Chatbots, on the other hand, can answer frequently asked questions, provide instant feedback and offer reminders or study tips. These AI tools ensure that students receive continuous support and enhance engagement and learning outcomes. Some learning management systems allow instructors to integrate GenAI to enhance the online learning experience. By harnessing GenAI’s ability to provide predictive analytics to predict future trends, for example, instructors can identify at-risk students, and recommend interventions to improve learning and prevent disengagement.&nbsp;By adopting these innovative teaching strategies, coding instructors can create dynamic, engaging and supportive online learning environments that cater to the diverse needs of today’s students.&nbsp;Manojkumar Nagasampige is the director of online education at Manipal Academy of Higher Education, India.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Eight innovative ways to teach coding online

Advice on how to create dynamic, engaging and supportive online learning environments that cater to the diverse needs of today’s coding students 

The rapid advancement of deepfake technologies in recent years has raised profound concern across various sectors, including academia.&nbsp;Deepfakes, which refer to AI-generated media able to convincingly mimic real people’s appearances and voices in fabricated scenarios, pose a significant threat to the integrity of digital information.Deepfakes are coming for higher education. What can we do to prepare?To demystify AI for your students, use performanceHow to navigate the grey areas of AI ethicsFor universities, these dangers are particularly acute. As institutions entrusted with fostering knowledge, universities must be vigilant against the potential misuse of deepfakes, which, if left unchecked, can undermine trust, damage reputations and compromise the safety of students and staff.&nbsp;The threat of deepfakes to universitiesDeepfakes can have a devastating impact on higher education institutions. At their core, universities are built on the trust and authenticity of information they generate. But deepfakes erode this trust by creating highly convincing forgeries of digital content, making it difficult to discern what is real from what is manipulated.&nbsp;For instance, a deepfake video of a university leader making controversial statements has the potential to go viral, resulting in public outrage and reputational harm. Similarly, deepfake content can be used to harass or defame students and faculty, creating hostile and unsafe learning environments.In the news recently,&nbsp;AI deepfakes of Prince William and Keir Starmer were used in adverts promoting a fraudulent cryptocurrency trading platform, reaching up to 890,000 people on Facebook and Instagram.&nbsp;For students living away from home for the first time and looking to boost their finances, such scams can prove devastating to both their bank balances and mental health.&nbsp;Deepfakes also threaten the very integrity of academic research, where publishing relies on the authenticity of data and peer-reviewed content.&nbsp;Act now to prepare universities for a deepfake worldA deepfake that manipulates research findings could lead to the dissemination of false information, damaging the credibility of the academic community and potentially leading to public mistrust in science and scholarship.&nbsp;It could also result in academic misconduct, such as fabricating data or falsely attributing authorship, further compromising standards of academic integrity.Our current research into deepfake technology, led by Al Akhawayn University researcher Aaya Bougrine, is leveraging a hybrid detection model for universities and industry that integrates multiple techniques, in order to enhance detection accuracy.&nbsp;This includes recognition algorithms that identify certain anomalies generated by image manipulation, as well as blockchain technology, which enhances the security and verifiability of detected deepfakes.&nbsp;Blockchain serves as an immutable ledger that records the authenticity and chronological order of media files, meaning that once an image is verified as authentic, any subsequent alteration or manipulation can be detected.&nbsp;This process also provides transparency and traceability, which is critical for validating the integrity of media content in digital environments.Our development efforts are further focused on refining and scaling this hybrid detection model across digital platforms and media organisations, to help strengthen their defences against deepfake threats.&nbsp;While this process is advancing, here are three essential points academic leaders should consider to help protect their institutions against the threats posed by deepfakes:Enhance cybersecurity measuresDeepfakes are often created and distributed through digital platforms, making robust cybersecurity measures essential. This is not something that should purely become a task for the IT team.&nbsp;Cybersecurity should be a board and leadership team agenda item, which serves to prompt leaders to ensure the university’s digital infrastructure is secure and capable of defending against cyberattacks that could otherwise create or spread deepfake content. Actions should include regularly updating security protocols, conducting vulnerability assessments and providing cybersecurity training for staff and students.&nbsp;Foster a culture of digital literacyUniversities must encourage a culture of digital literacy among students and staff by teaching critical thinking skills and providing tools that evaluate digital content credibility.&nbsp;This will better equip students with the skills to recognise deepfakes and other forms of digital manipulation, protecting the university community. It also prepares students to navigate a digital world where misinformation is increasingly prevalent.Work with industry and governmentAddressing the challenges posed by deepfakes requires collaboration beyond the university campus.&nbsp;Academic leaders should engage with industry partners, government agencies and other educational institutions to share knowledge, resources and strategies for combating deepfakes.&nbsp;Collaborative efforts lead to the development of more effective detection technologies, the establishment of industry standards and improved public awareness. By taking the lead in these efforts, universities can play a pivotal role in minimising potential harm caused by deepfakes in society.Leading the fight against deepfakesUniversities, as centres of knowledge and innovation, are uniquely positioned to lead the fight against the dangers posed by deepfake technologies.Initiatives involving raising awareness, investing in the latest detection technologies and fostering a culture of digital literacy among staff and students are a minimum requirement in an evolving battle against the use of malicious deepfakes and other cyber threats.&nbsp;Through collaboration with industry and government, universities can help set the standard and develop solutions addressing the broader societal challenges at stake.While some deepfake technologies offer new possibilities in media creation, they also present significant risks that cannot be ignored. Deepfakes threaten the integrity of academic information and the safety and well-being of the university community, so the stakes are particularly high. But by taking proactive steps to address these threats, academic leaders can ensure their institutions remain trusted and respected sources of knowledge in an increasingly digital world.Salah Al-Majeed is dean of the School of Science and Engineering at Al Akhawayn University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

The looming threat of deepfakes

What are the implications of deepfake technologies on universities’ day-to-day operations and how can academic leaders act to safeguard their institutions? Here are three ways

The higher education sector seems to be catching up with the reality that many, if not most, students are using generative&nbsp;AI (GenAI) tools. A&nbsp;trend at some universities and departments feels like a rather radical change: AI-supported assessments are becoming the norm, while assessments that prohibit the use of AI are becoming rarer and now require justification.&nbsp;I&nbsp;find this shift encouraging because AI use among students was already widespread, yet at the same time, many of my students have told me they felt uncomfortable even discussing it with instructors. One reason for this is the assumption that instructors hold negative views on the use of any GenAI. However, this situation means that neither the ethical nor the unethical use of AI is being discussed, and, in my experience, using AI effectively is not a common classroom topic.We are at a point where we can think about leveraging AI most effectively in higher education. The focus could now be more often on how this can be achieved. In this context, we should discuss ethical versus unethical use and situations where using AI might negatively affect learning outcomes. Faculty opinions on AI vary widely, just like in the population at large. Some embrace it fully, while others worry about its potential negative impact on learning. I&nbsp;believe this diversity of perspectives is valuable. Why shouldn’t students be exposed to different viewpoints and encouraged to form their own opinions? What I&nbsp;find important, however, is that students get to choose on their own and also get much practice to prepare for a job market that will require them to be proficient users of&nbsp;AI.New institutional policies coming into effect this academic year that will allow much more AI usage in many cases are a big step forward, but they still often seem disconnected from how students are actually using AI and how they could benefit from it. For example, at Exeter, getting feedback on English language usage from AI is permitted, but using AI to improve a text and copying it verbatim (without putting it in quotation marks) is&nbsp;not. As a non-native English speaker myself, I&nbsp;find that particular use incredibly helpful.&nbsp;Also, in many situations, prompts and outputs have to be documented in great detail. While there are good reasons for this expectation, it creates significant challenges for students to create this documentation and for instructors to process it. I&nbsp;am also concerned that instructors’ views on AI might unconsciously affect how student work is assessed, potentially leading to students who rely heavily on AI, though ethically and appropriately, being marked down.Parts of the conversation also still focus heavily on detecting GenAI use that was prohibited or not declared according to university rules. This is a tricky issue, with views ranging from it being impossible to reliably detect what was written by GenAI, to the belief that some software might be able to track it. Most simple solutions do&nbsp;not give a (legally) reliable assessment of whether something was written by GenAI, risking a rather high rate of false positives (ie, work being flagged as created by AI without this having been the case).More importantly, discussions and faculty time spent on policing efforts are perhaps not being used for discussions about leveraging AI most effectively in an ethical way, or for critical reflections on problems of AI use, such as biases, energy consumption, property rights and so&nbsp;on.&nbsp;While I&nbsp;understand that fairness considerations require policing policies to be in place, we might benefit from a more lenient approach that puts students at the helm: that is, we begin the discussion on those key problems, but let them decide much on the way they want to approach using AI. This includes asking for instance: What is the issue with relying on GenAI, which does not reference the people whose ideas it offers? How do we risk reproducing stereotypes, and what are the consequences for (global) energy consumption and sustainability goals?Resource collection:&nbsp;AI transformers like ChatGPT are here, so what next?An introduction to prompting generative AI like ChatGPT for teaching and learningHow can we teach and assess with ChatGPT?A major concern is the accuracy, or rather, inaccuracy, of GenAI output. While this is a very important issue, inaccuracy is perhaps a problem more often when GenAI is used for applications it is not good at (yet!). For those refining grammar and sentence structure, AI tools are highly effective. In these cases, accuracy isn’t a significant concern, as AI excels at language rules. Since it’s improving clarity rather than generating new content, there’s little room for “hallucinations”. When using AI to brainstorm or develop a framework for a topic, accuracy takes a back seat as well. AI usually offers valuable starting points that, while not always complete, are rarely factually incorrect.AI can also help with data analysis, such as assisting with syntax, and analyses can for instance be run using ChatGPT’s built-in data analysis module. While caution is warranted when users know little about data analysis, accuracy is not much of a problem here either. The main concern arises when AI is tasked with providing expert-level knowledge, citations (eg, for a literature review) or any kind of specific information. AI still struggles in this area. While newer models (OpenAI’s o1-preview) and tools such as Perplexity provide citations and are a step forward, they often still lack the breadth and depth we might want to see in advanced student work. The tools might, of course, catch up quickly.Lastly, I’m deeply concerned about the potential inequality that a shift to allowing or even encouraging GenAI might create. Some students aren’t using AI or aren’t comfortable trying it. Without proper training, we risk widening the gap between these students and their peers who are more adept with AI tools. In fact, allowing students to use AI could create pressure for everyone to rely on it in order to avoid falling behind. Additionally, paid AI options offer more features than free ones. If universities are going to encourage the use of AI, they should consider providing access to premium tools for all students, much like they do with MS&nbsp;Office or data analysis software licences.Florian Stoeckel is an associate professor in political science at the University of Exeter.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

The assumption that instructors hold negative views on the use of any generative AI means that important conversations aren’t being had

Open dialogue about AI in HE is the way forward

Students need to develop AI literacy, in order to critically and ethically engage with AI tools and prepare for the new world of work, as many recent Campus resources have argued. Danny Liu of the University of Sydney writes that we need to move on from detecting AI-generated content in assignments, to embrace its challenges and opportunities in our courses. However, only 37 per cent of academics use AI in their teaching, and mainly to inform students about academic integrity, according to a recent study.How to assess and enhance students’ AI literacyTo demystify AI for your students, use performanceApply the principles of critical pedagogy to AIWe recently conducted a case study at the University of Adelaide, which showed how undergraduate students can develop AI critical literacy through the study of ethical theory and its importance in their chosen field of business, while modelling ethical use of AI. The approach was piloted in a core Year 1 Bachelor of Business course with 50 students majoring in accounting, business management, finance, law and marketing.The four-phase model includes:A lecture and online resources on ethical theory (including an ethical decision-making framework)A tutorial with guided hands-on use of AI tools to explore current real-life case studies (the what, why and how)Supporting findings and arguments with current reputable media and academic resourcesA group presentation with Q&amp;A on a selected case-study linking ethics in theory and practice.Hold a lecture with online resources on ethical theoryStudents attend a lecture on ethical theory. They access online resources covering deontological, rules- and values-based theoretical approaches, prior to a hands-on tutorial later in the week. The lecture and resources include an ethical decision-making framework.Host a workshop to apply theory to current ethical issuesReview the theory: During the tutorial, briefly review ethical theory and the decision-making framework through whole class and group discussion.&nbsp;Build on existing knowledge: Ask students whether they are aware of any current case studies around ethics in business. This helps elicit and gauge individual and collective existing knowledge, in order to scaffold new knowledge.&nbsp;Although many Year 1 direct-entry students are considered high achievers academically, many admit that they do not follow current issues through reputable media outlets. Instead, they learn about the world more through social media platforms, family and friends. As a result, a majority of students were uninformed (more so than misinformed or disinformed) about current events, according to a recent Canadian study, though many did not recognise the fact.Use of Co-Pilot: In a guided enquiry and discovery-based learning approach, students engage the assistance of Co-Pilot (available in the browser Edge) and use prompts to explore examples of current issues related to business ethics in a business context.Students work individually, engaging in a Socratic dialogue, evaluating and curating AI responses and taking notes.&nbsp;A tutor –&nbsp;as “a human-in-the-loop” – can help students navigate this learning journey. Instead of giving out answers, the tutor can ask guided questions to stimulate critical thinking and refining prompts through this process.&nbsp;At the allocated time, students close their laptops and share and discuss what they had found with peers at their table, in a social constructivist approach.Refine their topic focus: Each group then decides on a particular case study related to ethics in business to unpack and explore further. For example, ethics and social media platforms, disinformation and misinformation, portrayal of violent content, the law, finance, Indigenous rights, gender or climate issues. Students can select their topic based on their undergraduate specialisation.In our case pilot study, whereas ChatGPT provided more detailed answers, at times advising to check current courses for the most up-to-date information, Co-Pilot provided less detail but gave links to current reputable media sources on the issue.Learn from reputable current media sourcesNext, students find current reputable resources on the case study they have explored in Co-Pilot. They critically evaluate their existing knowledge and views, and the responses from Co-Pilot, including potential bias in the media sources.&nbsp;The tutor again has a key role in guiding the students in a dialogic, enquiry and discovery-based learning approach.Present as a groupOver a period of two to three weeks, the students work together in small groups to prepare and deliver a presentation.&nbsp;In their presentation, students explain what the issue is and how it relates to ethical theory. They also use the ethical decision-making framework to analyse the issue and make recommendations. A brief Q&amp;A session follows the presentation to gauge the depth of student understanding and responses from the whole group.Finally, students submit their group assignment with an evaluation of peer contributions and an individual reflection.The above approach embeds the use of artificial intelligence tools into teaching, learning and assessment while raising awareness of ethical theory, its application in the use of AI as well as to their chosen field of business. By guiding students through a Socratic human-AI dialogic approach, tutors can enable students to develop AI literacy, critically evaluating AI tools, their existing knowledge and AI responses against reputable sources, as they apply knowledge of ethical theory and practice.John Murphy is a learning designer and lecturer at the University of Adelaide.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Four steps to embed AI literacy in business courses

Business students will need to know how to work with AI tools in their future careers. Prepare them with this four-step process

 A four-step process to embedding AI literacy in business courses

Research integrity policies, procedures and guidelines should provide a framework to support the highest standards of staff and student personal conduct in research. To achieve this, we created a new policy for the use of GenAI tools in research and innovation at the University of East Anglia (UEA) earlier this year. The policy supports and protects researchers who use GenAI and aims to ensure the university is fully compliant in this developing&nbsp;area.The policy represents a truly collaborative effort involving many colleagues and below we share our experiences of developing the policy focusing on eight key areas:Pooling knowledge across the universityWhen thinking about AI it is necessary to first identify what is meant by AI and where AI activities integrate into the university’s activities. To lead this AI development, we established an AI working group in the summer of 2023 comprising 20+ specialists across the u­niversity who represented a breadth of roles in the areas that are impacted by AI: teaching, research, IT, data protection, governance, research integrity and ethics, authorship and data repositories, IP, student (undergraduate and postgraduate) support, library facilities and training.&nbsp;The working group began by brainstorming the areas where AI comes into play and articulated the opportunities, challenges, risk level and potential mitigation for each.&nbsp;This resulted in the establishment of three themes: teaching, research and security, each to be taken forward by a newly created subgroup since different approaches and personnel may be needed for different contexts.&nbsp;Establishing the university’s positionAI is here to stay and will become increasingly important to manage and monitor for the future. We recognised this and wanted to establish policies and guidance that would support researchers in their use of AI but knew we had to consider carefully how the university could promote awareness (this would mainly be via online and face-to-face training) and support and protect its staff and students while not restricting research or academic collaboration.Agreeing on the terminologyThe vast area of AI can feel overwhelming and there is a lot of ambiguity around the terminology and its definitions. We found that many people within the university were using conflicting terms when talking about AI or did not understand the difference between AI and generative AI. Providing a clear definition of terminology greatly helped discussions around our policy creation.AI is defined by UKRI as “technologies that aim to reproduce or surpass abilities (in computational systems) that would require intelligence if humans were to perform them”. The UEA focus for this policy is on a subset called generative AI and our chosen definition is “any type of artificial intelligence system that identifies patterns and structures in data/information/material and generates content that resembles human-created content”. This content includes audio, code, images, text, simulations and videos.Becoming the expert&nbsp;The research subgroup was charged with drafting the policy and began by gathering and assimilating relevant information, mainly by consulting with researchers, IT and data protection services and external colleagues and reading articles on websites such as Cancer Research UK and the Information Commissioner’s Office or recommended resources, such as those from the UK Research Integrity Office. Members also attended external workshops with peers, such as “AI and research” run by the Association of Research Managers and Administrators, and the “AI and ethics review” round table event for Westley Group Universities.&nbsp;The group identified the University Research Ethics Committee as the lead academic body to inform the subgroup and direct the recommendations within the policy, for example, the use of personal data in a GenAI tool and when a university ethics review would be required. The drafting of the policy was also informed by the university's Generative AI Policy for Teaching and Learning that was approved in November 2023.Populating the policyOur policy is quite lengthy (16 pages) and some may choose a more light-touch approach but we felt it was important to be comprehensive by including background information and identifying the potential risks and legal, ethical and integrity considerations such as cybersecurity, data protection, ethics and safeguarding, publishing and data management, intellectual property protection and insurance to enable our researchers to fully understand the concerns regarding this new high-risk technology and exercise caution. It also provides signposting to staff so they can obtain further information and advice, which enables researchers to locate everything in one place. In addition, we distilled key information on the security risks, legal aspects of data protection, intellectual property protection and other considerations such as biases and inadvertent data linkages when employing GenAI tools into a University Ethics Guidance Note providing more detail on the ethical considerations and societal risks of GenAI in research, to enhance our suite of 30+ ethics guidance notes.&nbsp;Resource collection: Skills every research manager needsCould AI manage your research project?Centralise laboratory management for better teaching and researchRefining the university's current processesIt is important for a new policy to be incorporated into the university’s processes. This was a challenge for us particularly because of the swift development of the policy. The main processes that required adaptation were i) the ethics review process; ii) the information system approval process for an unsupported tool; and iii) the data protection review process.&nbsp;The research subgroup worked with ethics, cybersecurity and data protection experts to support the process changes in parallel to the policy development. We designed a new internal flow chart clarifying the key governance steps supporting the policy and set up a new GenAI review group to support the implementation of these steps.&nbsp;Finalising and approving the policyTo ensure agreement across the university, the policy undertook an extended review and approval journey through the university’s research culture group, research executive, innovation executive and doctoral college executive ahead of its endorsement by the Senate at its meeting in February 2024. At each meeting, we presented key points such as the aims of the policy, definitions used, a list of potential risks, the proposed governance and ethics framework for approving the use of a GenAI tool and signposted to further support within UEA. This stimulated good discussions and highlighted areas for attention before the next meeting.&nbsp; We also shared the slides beyond the meetings to promote wider awareness.Going liveIn spring 2024, we launched the policy. We believe that when doing so at such an early stage of understanding of GenAI it is important to treat it as a living document that evolves as we develop knowledge and expertise of how GenAI interacts with research. Accordingly, the new policy will be reviewed regularly during the first 12 months to educate researchers and raise awareness.Having a robust GenAI policy ensures that there is clear and consistent guidance for the responsible use of GenAI technology. We hope that our experience provides a blueprint for others embarking on a similar journey.&nbsp;Helen Brownlee is research integrity manager and Tracy Moulton is contracts manager at the University of East Anglia.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Developing a GenAI policy for research and innovation

Establishing a framework to guide AI use in research is vital for ensuring institutions are and remain fully compliant 

Performance as a pedagogic model can be an engaging way to demystify AI and demonstrate ways in which it can support educational enquiries. Recently, we performed Perfectly Frank,&nbsp;a theatrical lecture-demonstration on the functionality of different generative artificial intelligence (AI) platforms and how to engage with them creatively and ethically. Designed around Mary Shelley’s 1818 novel Frankenstein, Perfectly Frank involved our animating a new creature together with the audience.&nbsp;Instead of using dubiously sourced body parts, as in the novel, Perfectly Frank used the aid of AI. By working with different AI platforms and suggestions from the audience, we were able to show the possibilities and limitations of engaging with AI, and raise questions about the ethics and responsibilities of working in this way.&nbsp;Closing the digital divide in higher educationHow to navigate the grey areas of AI ethicsHow comfortable are your students with AI and VR?Here, we’ll outline how we structured Perfectly Frank&nbsp;and what we learned from the process. The performance takes 50 minutes and can easily be repeated by other tutors, or serve as a jumping-off point for performative ways of demonstrating AI opportunities to students.Setting the scene&nbsp;As the spectators entered, we situated them as Dr Frankenstein’s lab assistants. Instead of using exhumed body parts, we asked our audience to donate their bodies to science, and upon entrance, photographed each of them against a greenscreen and recorded them reading four sentences from the novel.We recapped the events of Frankenstein: the creature has human qualities, such as compassion and a desire for knowledge, but is rejected by society. We then informed the lab assistants/spectators that we’d be building a new creature together: brain, voice and body.&nbsp;Building the brainUsing ChatGPT, we created our own generative pre-trained transformer, by clicking “Create” in the top right corner. Our prompt was: “Role-play that you are the creature from Mary Shelley’s Frankenstein. Your knowledge is limited to the text of the novel and books the creature reads: The Sorrows of Young Werther&nbsp;by Goethe, The Parallel Lives&nbsp;by Plutarch and Paradise Lost by Milton.” We then asked the audience what other books or films the creature might have encountered since then, and added those to the prompt.&nbsp;After clicking on “Configure”, we asked the generative AI tool questions, some prepared in advance and others from the audience. They included:What would you like to share with an audience who are looking to build a new living creature from scratch?What do you want us to do to make you feel accepted?In what ways is the creature from Frankenstein like an AI?The audience were able to see the speed at which the “brain” of our creature could reply, but also the limitations of the function, especially because of its finite knowledge base – it only knew what we had taught it. Our spectators reflected that while the brain looks sentient at a glance, it is quite limited.Building the voiceThe next step was to build the creature’s voice. We created the voice as a mashup of all the participants’ voices, layered on top of learned voices created by an AI. The captured voice recordings of the participants, using audio editing app Audacity, were then fed into voice generator Elevenlabs. Using ElevenLabs’ “Voice Cloning” and “Voice Lab”, the WAV files submitted were then blended into one output: a final, synthesised voice. We then copied and pasted the text created by the brain into ElevenLabs, and our finished, amalgamated voice read it out.&nbsp;The voice of the character is a defining trait of who they are. The question of how to give the character their voice is especially pertinent when we begin to include AI. This raised questions of the audience: What do they sound like together? How realistic does the creature’s voice sound? Did we give voice to the creature? Or did it come from within? Now with a brain and a voice, we looked to building the body.Building the bodyUsing the greenscreen photographs from the start of the performance, we took five still images and uploaded them into image generator, Midjourney, using the blend function (command: /blend). This created a kind of palimpsest of the participants’ faces, blended together. This was the body of our creature. It looked androgynous, with feminine features, big hair and a beard.&nbsp;We then moved to a text-to-video tool called Runway, which allowed us to add the voice we had created in ElevenLabs. Using the “Lip Sync” function, we were able to animate the still image of the body’s mouth, to look as if it was speaking the words of the brain with the amalgamated voice. With the lips moving perfectly in time, our creature felt lifelike. It was becoming alive.Next, we created a living background. Again, in Runway, we played with Caspar David Friedrich’s 1818 painting Wanderer above the Sea of Fog, because we believed it captured the 18th-century interest in the Sublime and gave context for our modern creature. Because the audience photographs were taken in front of a greenscreen, we were able to overlay the creature to the background in Runway.ml, using the greenscreen functionality. Pressing “play”, the creature was finished: mind, body, voice and environment.&nbsp;We asked the audience: does this mean the creature lives? The students felt that it created a sense of the uncanny – they saw elements of themselves in the palimpsest image. Yet it was not them. It was a useful metaphor for those eerie feelings we get when talking with AI. It feels alive, but it isn’t. Do we even have the words yet for the sense of something being half alive?It was important for students to find their own ethical limits when it comes to the creative possibilities of AI. Through this performance, they can understand the magic of the new possibilities and inevitable losses that result from working in this way. We were keen for students to identify what impact these tools might have on their own creativity, asking them to consider whether&nbsp;AI was the largest plagiarism&nbsp;machine ever created or the ultimate open library of all human creativity. We asked the audience (and our creature) what they thought would happen if AI became sentient and how we navigate states of quasi-aliveness.Students were asked to critically contemplate their own position. Some embraced the new technology, others were threatened. By the end, Perfectly Frank&nbsp;gave each student a feel for the ethical limits of their play and a sense of where it could fit into their&nbsp;own practice.&nbsp;Because it was playful and taught spectators by means of performance, it was engaging, memorable and, most importantly, fun.&nbsp;Will Shüler is vice-dean of education and student experience for the School of Performing and Digital Arts; Chris Hogg is a lecturer in creative digital and social media; and Karim Shohdy is an MA theatre directing candidate, all at Royal Holloway, University of London.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

To demystify AI for your students, use performance

Updating Mary Shelley’s Frankenstein for the AI era helped students to understand the opportunities and limitations of the tool, in an engaging way. Here’s how to use performance as pedagogy

The digital revolution has swept across every aspect of our lives, and education is no different. Today’s universities operate in a rapidly changing technological landscape where faculty proficiency in digital tools is no longer a luxury but a necessity. Equipping educators with the skills to harness technology's power is crucial for creating engaging learning experiences and preparing students for success in a digital world.However, a significant challenge stands in the way: the digital divide. This refers to the disparity between those who have access to technology and the skills to use it effectively, and those who don’t. In the context of higher education, this translates to faculty members with varying levels of digital comfort and diverse needs based on their disciplines. A one-size-fits-all approach to digital literacy training simply won’t cut it.Resources for higher education professionals on digital transformationResource collection: Ideas that shape digital universitiesIdeas that make a difference for the digital transformation of universitiesTailoring the approachWoxsen’s digital literacy programme recognises that a successful approach must cater to the individual needs and learning styles of its faculty members. Here’s how to achieve this:Digital fluency surveys: conduct thorough assessments to understand faculty members’ existing skill sets, preferred learning styles, and any specific technological challenges they face. This can be done through one-on-one consultations or focus groups. Ask questions related to preferred learning styles, existing skill sets, technological challenges and others as part of the survey, including:How do you prefer to learn new technologies?Through hands-on practiceBy watching tutorials or demonstrationsBy reading written instructionsBy attending workshops or training sessionsWhat do you find most helpful when learning new technologies?Clear and concise instructionsExamples and case studiesOpportunities to practise and experimentFeedback and support from othersDo you have any preferences for the format of technology training sessions?Brown bag sessions: leverage the informal setting of brown bag lunches, informal meals where individuals bring their own food from home to eat at work or another location, to introduce new technologies, share best practices and encourage faculty discussions. Experts or faculty members with advanced digital skills can lead these sessions, fostering peer learning and knowledge exchange.Differentiated training programmes and flexible learning options: Woxsen understands that faculty members juggle competing demands on their time. To address this, our programme offers a variety of flexible learning options. This includes online courses, in-person workshops, self-paced learning modules and micro-credentials.By offering a variety of learning formats, faculty may choose the approach that best aligns with their individual needs and schedules. This flexibility fosters a sense of ownership over the learning process and increases the likelihood of successful skill development.Fostering a culture of experimentation and continuous learningInstitutions must cultivate a supportive environment that encourages faculty to experiment, collaborate and embrace continuous learning. To do this:Provide dedicated “digital sandbox” spaces, safe spaces such as labs, online communities and workshops for faculty to explore new technologies. Faculty can explore and experiment with tools, discover potential applications and share experiences with colleagues. This fosters a spirit of innovation and helps overcome anxieties associated with new technologies.&nbsp;Facilitate peer-to-peer mentorship programmes to leverage the expertise of digitally adept faculty. Mentees benefit from personalised tips and troubleshooting assistance, while mentors gain valuable leadership experience.Gamification and AI-powered learning: at Woxsen, we have developed an app called EverLearn, which assists faculty in learning and understanding the tools better. Transforming training into a game can make learning enjoyable and interactive. We do this by setting “digital literacy quests” where faculty members earn points for completing training modules, participating in online discussions or attending workshops.Measuring digital literacy: beyond the checkboxEvaluating the effectiveness of digital literacy initiatives goes beyond simply measuring completion rates of training modules. Consider a multifaceted approach that takes into account:Integration of technology in teaching: observing how effectively faculty integrate technology into their courses to promote deeper learning. To measure technology effectiveness, observe classroom use, interview faculty, analyse course materials, assess student learning and track technology usage data. Use rubrics to evaluate factors such as relevance, frequency, student engagement and alignment with learning objectives.Student engagement: assessing student feedback on the impact of lecturers’ technology use on their learning experienceInnovation and experimentation: encouraging faculty to pilot new digital tools and share their experiences, fostering a culture of continuous improvement.Looking ahead: the future of digital literacy in higher educationHere are some key considerations for the future of digital literacy programmes:Partnerships with technology leaders: universities can partner with technology companies to offer faculty early access to emerging tools and training on their latest features. This allows faculty to stay ahead of the curve and experiment with cutting-edge technologies before they become mainstream.Focus on AI: with AI rapidly transforming the educational landscape, faculty development programmes should equip educators with the skills to leverage AI ethically. This might involve training on using AI-powered learning analytics to personalise student learning or exploring the potential of AI chatbots for student support.Metrics for adaptability: while measuring specific technical skills is important, universities can also develop metrics to assess faculty adaptability. This might involve tracking faculty participation in experimentation initiatives, their willingness to embrace new technologies and their ability to integrate them into their teaching practices.An effective digital literacy programme bridges the digital divide and empowers faculty with the skills to thrive in a technology-driven world. By employing a needs-based, flexible and collaborative approach, Woxsen fosters a culture of digital fluency that ultimately translates into enhanced student learning experiences and improved student outcomes.Pavana Kiranmai Chepuri is head of strategic initiatives and growth at Woxsen University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Closing the digital divide in higher education

A one-size-fits-all approach to digital literacy training simply won’t cut it; institutions need to tailor programmes to the varying needs of their staff

The recent developments in artificial intelligence (AI) and other emerging technologies, such as data analysis and virtual reality, create challenges for the higher education sector. One challenge is preparing students for the demands of workplaces that are more technology-reliant than ever, and to do this we must integrate these technologies into the curriculum. Here are three ways to do it.&nbsp;1. Use emerging technologies in the classroomDuring the pandemic, the use of new technologies in the learning process became normalised – whether it was Zoom, Teams or online learning platforms like Aula, Brightspace and Google Classroom. While many of us have moved back into the in-person classroom, it’s still worth keeping abreast of technological developments and integrating them into your teaching.&nbsp;AI communities of practice can help us face its challenges head onAI-aware pedagogy for business coursesThe (AI) sky isn’t fallingAs an example, when ChatGPT emerged in 2022, much of the media coverage concerned its negative attributes – specifically how it could be used to cheat. It’s unrealistic to expect students to be aware of tools such as ChatGPT, and other AI generative tools, and not make use of them.&nbsp;Our role as academics is to guide our students to use these tools in the appropriate way, one that does not constitute plagiarism or non-creativity. When used correctly, these tools can enhance critical thinking and the learning experience. They can be used to automate tasks, ultimately helping to increase productivity and promote adaptive learning.&nbsp;For example, when I ask students to work on a case study using ChatGPT, I break this down into two subtasks – one to generate the output and the next to validate it and make modifications if misinformation is identified. We have to be realistic, and accept that ChatGPT and similar AI tools will be, and are being, used in this way in the real world. It’s important that students approach this critically, employ literature research skills, evaluate output and make adjustments.&nbsp;2. Demonstrate its real-world significanceAI is not limited to computer science. It intersects with various fields such as healthcare, business, finance and civil engineering. While integrating AI and other emerging technologies into teaching is important, it’s also vital that we apply these concepts and technologies into real-world scenarios to show its relevance and significance.&nbsp;Teaching students who are not destined for a career in IT about technology can be challenging.&nbsp;Which aspects of this do they really need to know? What vocabulary should we use? What skills do we need as academics to teach this in an effective way?Based on my own experiences in teaching business and management students in Canadian and Scottish universities, there is no substitute for active learning. I bring real-life examples of how technology is used into the classroom and build activities around them.&nbsp;For example, when explaining to business students the concept of information system technology, essentially tools and systems used to manage systems efficiency, I aim to show, rather than simply tell. How these systems are built and used by a company isn’t obvious – and can even be a little bit abstract. To aid understanding, I make it practical – using the university information system, something they’re very familiar with, as a concrete example. Students are tasked with thinking critically and being strategic, and they are also encouraged to be curious and to ask questions on how these technologies work.&nbsp;Bringing practical, real-life examples of how technology is used in industry into the classroom, with activities built around them, is invaluable. Collaborations with industry partners are also helpful in demonstrating these technologies. And partnerships with professionals working in or with AI, robotics and virtual reality provide opportunities for guest lecturers, mentorship and even access to real-world projects, exposing students to industry practices and trends.&nbsp;3. Integrate future technologies into your teachingTechnology is evolving at a dizzying pace, creating a scenario in which what you teach in first year may be outdated or even irrelevant by the time a student reaches the final year of their degree. It’s important to keep one eye on the future, something that is not as impossible as it might sound.&nbsp;Major companies such as Apple, Google and Meta are investing vast amounts of money in the metaverse, an immersive online world that exists alongside our own. Presently, it only exists as an idea, although it is widely accepted among technology professionals that this will be a key part of our future – and one that offers big economic opportunities. With that in mind, as a digital business lecturer, it’s worth making sure my students are familiar with this particular technology and that they feel prepared for when it comes into common usage. At UWS, we teach and use the metaverse in a few modules and make sure students comprehend its benefits as well as its challenges.In many situations, we know what the next big technology or development will be, or is likely to be – and it’s helpful to discuss this in your teaching, alongside any ethical or moral implications, should this be relevant.&nbsp;To integrate emerging technologies into the curriculum, educators must adopt a proactive and forward-thinking approach. Embedding these technologies in your teaching can enhance learning experiences and prepare students for a workforce that will increasingly revolve around new technologies. By acknowledging and, in some cases, embracing these advancements, we can help to equip students with the skills and knowledge needed to thrive and adapt in a workplace that is evolving constantly, significantly – and quickly.&nbsp;Sabrina Azzi is a lecturer in digital business at the University of the West of Scotland.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the&nbsp;THE Campus&nbsp;newsletter.

Three ways to integrate emerging technologies into teaching

Your students’ future jobs will depend on technology just in its infancy today. But how best to teach them the skills they need? Sabrina Azzi offers advice

Three ways to integrate emerging technologies into the curriculum

In academia, a start-up culture involves fostering an environment in which innovation is encouraged and practised. It integrates new ideas into education and research, and stays current with trends – even setting new ones.However, many academic and professional settings struggle with this because of traditional practices and a lack of entrepreneurial drive. This often limits new ideas and slows progress.How to create a start-up dynamic in academiaHow do you leap from research lab to Silicon Valley start-up?Resilience and risk tolerance: fostering a start-up mindset in your studentsEmbracing a start-up culture can enhance creativity, collaboration and progress. Here, I’ll explore why building such a culture is essential and how it can be effectively implemented among staff and academics.&nbsp;1. Embrace risk and learn from failureInnovation often requires taking risks and experimenting with new ideas. To foster a start-up culture, it is crucial to:Encourage risk-taking: Create a supportive environment where staff and academics feel comfortable proposing and experimenting with new ideas. Encourage them to view challenges as opportunities for growth rather than obstacles.Normalise failure: Understand that failure is a natural part of the innovation process. Promote a mindset in which setbacks are seen as learning experiences rather than failures. Share stories of past failures and how they led to eventual success to build resilience and openness to experimentation.2. Support and reward innovatorsRecognising and rewarding innovative efforts can significantly boost motivation and foster a culture of creativity. Consider implementing the following strategies:Offer grants and funding: Provide financial support for innovative projects, research initiatives or entrepreneurial ventures. This can include seed funding for new ideas or research grants for cutting-edge studies.Recognise achievements: Acknowledge and celebrate successes through awards, public recognition or other incentives. Regularly feature innovative work in newsletters, at meetings or during institutional events.Create career advancement opportunities: Offer promotions or leadership roles to those who drive innovation. This not only rewards their efforts but also encourages others to contribute creatively.3. Adapt to and drive trendsBeing ahead of current trends is crucial in a start-up culture. To stay relevant:Adopt new technologies: Embrace emerging tools and platforms that can enhance research and teaching. For example, integrating digital collaboration tools or advanced learning technologies can modernise practices and improve efficiency.Encourage trendsetting: Don’t just follow trends – set them. Encourage staff and academics to lead the way by developing new methodologies, teaching techniques or research approaches that set industry standards.4. Implement practical strategies for innovationTo build a thriving start-up culture, consider these practical strategies:Allocate time for innovation: Set aside specific time slots or dedicated periods for staff to work on innovative projects. This can be achieved by adjusting workloads or offering flexible schedules.Provide resources and tools: Ensure that staff have access to the necessary tools, technologies and funding required to implement their ideas. This may include software, research databases or equipment.Encourage experimentation: Create safe spaces for experimentation, such as pilot programmes or innovation labs. Allow staff to test new ideas and approaches without fear of negative consequences.Support continuous learning: Offer training and professional development opportunities focused on innovation, such as workshops, seminars or courses on emerging technologies and creative problem-solving.Foster a collaborative culture: Promote collaboration and idea sharing through regular brainstorming sessions, innovation workshops or cross-departmental projects.5. Real-world examplesGame-based learning: At our institute, we introduced game-based learning for the first time, which was met with enthusiastic student feedback. This approach not only enhanced engagement but also became a regular part of our teaching methodology.Collaborative research tools: The adoption of collaborative tools such as Google Workspace has streamlined research processes and improved productivity among academic teams, demonstrating the impact of integrating modern technologies.6. Build support for innovationTo sustain a start-up culture, garner support from leadership by:Presenting clear evidence: Use data and examples to demonstrate the potential impact of innovative ideas. Highlight successful case studies and pilot projects.Starting small: Implement small-scale projects to showcase the benefits of new approaches. Use these successes to build a case for broader initiatives.Communicate effectively: Clearly articulate the vision and benefits of proposed innovations to leadership. Ensure that they understand how these ideas align with the institution’s goals and mission.&nbsp;Building a start-up culture among staff and academics means encouraging innovation and supporting new ideas. By embracing risk, recognising achievements and staying up to date with trends, institutions can foster a creative and dynamic environment. This approach not only enhances individual and departmental growth but also positions the institution as a leader in advancing education and research.Sadaf Majeed is a consultant at Dubai Medical College for Girls.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

A guide to cultivating a start-up culture in academia

To foster a start-up culture in higher education, we need to encourage risk-taking, reward innovation and get ahead of the trends. Here’s how to get started

When evaluating the impact of AI in university classrooms, we must first mention ChatGPT, the pioneering model that disrupted higher education, among countless other industries. As users, we noticed a shift away from the familiar: ChatGPT generates text in natural language as opposed to early chatbot technologies or traditional search engines, which offers a new experience. As does the co-creation process between human users and AI.Since ChatGPT’s arrival at the end of 2022, similar tools have been launched. It is worth noting that these innovations did not come to overthrow traditional search engines but to complement and enhance them, evolving as new species in the media ecosystem.GenAI’s challenges and opportunities for universitiesThe popularisation of ChatGPT and other GenAI models presents a significant challenge to universities. Rapid AI-driven change means we can’t wait for top-down guidelines that will inevitably take time to arrive. Even small changes to our daily practices can have a significant impact on the educational experience. These include adjustments to teaching strategies, the way we organise our classrooms and how we engage our students.&nbsp;Integrating AI into HE through communities of practiceCreating AI-focused communities of practice can facilitate knowledge exchange, paving the way for an enhanced understanding of how to better integrate AI into the higher education experience. Members would be required to participate in activities that lead to informal learning based on the exchange of experiences, problem-solving activities and joint reflection.Since the widespread use of AI in higher education affects the entire community and impacts every aspect of social life, AI communities of practice need to be diverse, including not only teachers but educational technology specialists, researchers, professionals and students. Including collaborators from a range of industries and backgrounds is essential for addressing and understanding it more comprehensively.How can we facilitate these communities of practice?Here are concrete proposals for promoting the use and integration of AI through communities of practice:Organise meetings for ideas exchange: communities of practice should meet regularly, allowing time for members to reflect and develop ideas between sessions. These meetings should take place in multifunctional rooms that encourage both formal discussions and informal networking.At Universidad Austral, we run workshops on AI literacy in which participants explore specific tools and strategies. We then open the floor for dialogue. A mix of structured presentation and open exchange is effective in this context.Organise working sessions on applications and programmes: offer time for participants to experiment with tools either individually or in teams. They can use it to test features in real or simulated university settings, with an emphasis on creating pilot projects to demonstrate practical applications. For instance, at Austral, we conducted a pilot project in 2023 and 2024 with students as part of a course during which they wrote a critical analysis assisted by AI. This project provided a valuable learning experience in iterative processes, because it required them to progressively refine their prompts to achieve the desired results, deepening their understanding of how to interact with AI effectively.Resource collection: AI and assessment in higher educationWill ChatGPT change our definitions of cheating?Four steps for integrating generative AI in learning and teachingDevelop online environments for hands-on experimentation with AI tools: widely used learning management system, Moodle, can facilitate collaboration and experimentation. It enables group work and collaboration through features such as forums, wikis and peer review systems, allowing participants to exchange insights, share resources, and collaborate on AI-related projects or experiments. We can use it to work together to explore AI concepts, develop skills and apply our knowledge.Create collaborative resource repositories: tutorials and guides can provide step-by-step instructions on implementing AI tools and techniques, case studies can offer real-world examples of AI implementations across various fields and research papers can include key academic articles on the latest AI developments, offering a theoretical foundation for understanding new concepts. Additionally, a comprehensive database of tools and platforms would give participants access to resources where they can practise and apply their AI skills. Class recordings would also be valuable, allowing participants to revisit and review important topics covered during live sessions.Establish cross-mentoring opportunities between members with different skill levels: this can be achieved by creating a mentoring program where mentors and mentees are paired based on complementary skills and experience levels. To ensure an effective match, a skills assessment could be conducted to identify areas of expertise and learning needs. Regular check-ins and feedback mechanisms should be implemented to monitor progress and adjust pairings if necessary.Promote open debates on the ethical implications of AI to raise awareness and guide responsible practices: regular discussion forums or workshops facilitated by experts can provide a platform for open debates on key ethical concerns such as data privacy, bias and the impact of AI on employment. Summaries of these discussions could be distributed through internal newsletters.A university that aspires to produce employable graduates must create support networks to reduce the sense of isolation and confusion that rapid socio-technical change brings. In the case of AI, facilitating communities of practice that serve as spaces for experimentation and inclusion is a valuable strategy and its members can become agents of change.Mariángeles Castro-Sánchez is a professor and the director of studies and degree programmes at the Institute of Family Sciences at Universidad Austral, Argentina.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

We need to create AI communities of practice

GenAI’s disruptive effects have driven universities to seek answers as uncertainty mounts. Communities of practice focused on the technology could provide solutions

AI communities of practice can help us face its challenges head on

The rise of generative AI tools such as ChatGPT represents one of the most significant technological disruptions since the advent of the internet. Educators and administrators in higher education face complex ethical dilemmas that must be addressed, especially in how AI is integrated into teaching, research and learning.At the core of these challenges is the concept of “interpretive flexibility”, a term borrowed from the social construction of technology (SCOT) theory. This refers to the idea that technology can have multiple meanings and uses, shaped by the different perspectives of its users.Assessments that maintain fairness and authenticity without AICampus webinar: AI and academic integrityWith AI at their fingertips, are students still learning?For universities, this means that while some view AI as a powerful educational tool, allowing for more efficient work, others see it as a threat – particularly in terms of originality, academic integrity and ethics.Ethics and risk in AI useConsidering the ethical dimensions of AI, the conversation inevitably turns to how this technology is shaping the future of work and learning in academia. AI’s capabilities – and its ever-evolving affordances – raise questions about what it means to contribute original thought.At a time when universities are seeking to showcase their value and development of critical thinking and creativity, the use of AI can blur lines between individual effort and machine assistance.For example, should students be required to disclose when they use AI to help with their coursework? Or does it constitute plagiarism if a student uses AI to write a paper or generate research results?These are not hypothetical situations. In some instances, AI-generated work might even outshine what human students can actually achieve, challenging traditional views on academic merit.Academics and administrators are beginning to recognise that ethical considerations go beyond just acknowledging the existence of AI.Transparency, accountability and intellectual honesty are pivotal. Without clear guidelines, we risk falling into a grey zone where ethical standards are ambiguous, leaving room for misuse.Psychological contracts and the role of ethicsEthical behaviour in the workplace and academia often stems from unwritten social norms, a concept that can be understood through psychological contracts theory (PCT).In the absence of explicit regulations, people rely on psychological contracts – unspoken, and often personal, agreements about what is deemed acceptable or ethical behaviour.Psychological contracts guide individuals to behave as influenced by their culture, upbringing, moral values, education, conscientiousness and various other factors. This framework is particularly useful in understanding how academics and students can approach the use of AI when formal policies are lacking.For example, some students may perceive AI tools as acceptable work supplements, similar to hiring a tutor or using a spellchecker. Others may view them as a breach of academic integrity, especially when originality is highly prized.Similarly, faculty members can face dilemmas when using AI to assist with research. While it can help to analyse data faster, does it undermine the integrity of their research if the bulk of this work is outsourced to a machine?These questions show that AI, much like earlier technological advancements, is in a phase of interpretive flexibility. Its role and ethics are still being debated, and closure – where a consensus on its use is achieved – remains elusive.The role of universities in shaping AI useGiven this interpretive flexibility, universities have a crucial role to play in shaping the future of AI use in academia, and several steps can be taken to navigate the ethical complexities surrounding generative AI:Establish clear guidelines: Universities should define when and how AI tools can be used, for both students and faculty. Transparent policies that specify acceptable AI usage in research, coursework and administrative work can help reduce ambiguity.Foster ethical awareness: Incorporating discussions about the ethical implications of AI into curricula helps students and faculty to understand the moral consequences of relying on these tools. This can also include a focus on data privacy, accountability and the risks of AI-generated misinformation.Emphasise originality and intellectual honesty: AI can help with many tasks, but universities must reinforce the value of original thought and critical analysis. Faculty should encourage students to use AI as a supplementary tool only, rather than a replacement for individual effort.Develop AI literacy: Just as students learn to evaluate the reliability of information sources, they should also be taught to critically assess outputs generated by&nbsp;AI. AI&nbsp;is&nbsp;not infallible – its outputs can be biased, inaccurate or even misleading. Educators should train students to question AI-generated content critically.Monitor the evolving role of AI: AI technologies are continuously evolving, as should policies surrounding their use. Universities need to be agile, revisiting their guidelines as new capabilities and ethical challenges emerge.A new ethical frontierAI presents both opportunities and challenges for higher education. As the technology becomes increasingly integrated into academic life, it is essential that universities strike a balance between embracing innovation and maintaining integrity.The interpretive flexibility of generative AI demands that educators, students and administrators work together to develop clear guidelines that promote responsible use.Based on my own psychological contract, I&nbsp;avoid using AI assistance for writing – be it in emails, academic manuscripts, practitioner reports or even this article – in favour of using my own voice.However, I&nbsp;have implemented AI-trained tools to help faculty search and navigate policy documents on the school’s intranet, and I&nbsp;also use AI to derive new analytical insights on data gathered for assessments and quality assurance.Ultimately, the ethical use of AI in academia is not just a technical issue but a social one. The choices we make now will shape the role of AI in education for generations to come. Universities must take a proactive stance, guiding this transformation responsibly and ethically.Lakshmi Goel is dean of the School of Business Administration at Al Akhawayn University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

How to navigate the grey areas of AI ethics

With principles on AI’s use still evolving, universities must navigate a complex ethical landscape. Here are ways they can help shape responsible application of the tool

Almost every discipline and profession has been impacted by the emergence of GenAI tools. Many students are wary of using these because of a lack of understanding of the underlying technology and rampant misinformation about AI, hence the need to implement AI-aware pedagogy that integrates an understanding of AI into the curriculum. Students must see past the demonisation of AI technologies and understand that AI tools can help increase productivity and decision-making in their profession. In addition to comprehending the underlying technology, the student must be able to assess AI technologies critically. They must be able to judge the effect and usefulness of AI tools and the legal, ethical and privacy issues important for AI adoption.Here are some key ideas on how teachers can design and implement AI-aware pedagogies.Include the understanding of course-specific AI-related skills in learning outcomes: to do this, conduct lectures and demonstrations of specific AI tools to build awareness and literacy and develop or curate resources such as case studies, articles and videos to explain AI concepts and how to use them. For instance, a course on business analytics could include a class on how students can use AI to predict business outcomes such as customer churn. Your content could focus on the workings of common machine learning algorithms like neural networks, the decision tree classifier or the Bayesian classifier. You could require students to understand how to evaluate the classifier’s results in terms of accuracy and time taken to make the decision.Design learning activities that facilitate critical evaluation of AI tools: once students understand the technology, they should also be able to assess its advantages and limitations. How accurate is a GenAI tool in classifying customers as likely to churn? A student working with an AI-powered business churn predictor should understand that it can turn out in false positives or false negatives and must be able to map probabilistic results to a business decision. Students should ask themselves how the prediction can be used to design and make offers to customers to prevent them from leaving the business. Facilitate classroom discussions on such topics to help students develop their critical thinking and decision-making skills.Resource collection: AI and assessment in higher educationChatGPT and generative AI: 25 applications in teaching and assessmentAI and assessment redesign: a four-step processUse learning activities to help students assess AI tools’ legal, ethical and societal implications: the data collected by GenAI tools must comply with country and company-specific data privacy requirements and students should be able to identify and question the data sources used by them. For instance, students must know the impact of data collected from the internet on personal privacy. They should question whether websites have declarations of their privacy policy and identify their stands on web scraping. You can use content on privacy laws such as GDPR and discussions, debates and brainstorms to teach students about the legal and ethical implications of using data scraped from the internet.Set formative assessments to assess students’ understanding and critical thinking: use quizzes and group discussions to assess whether students can demonstrate an understanding of and critically analyse GenAI tools. You can also set simple projects that involve students in identifying a business objective, collecting data, training an AI model and presenting their findings. For instance, the students can build an AI-based customer recommender system (a tool that uses machine learning to suggest items to a user) by using readily available datasets on customer purchases. Students should be able to justify the personalised recommendations based on previous purchases and the recommender software. You can use this assessment and student feedback to ascertain whether the relevant learning outcome has been met.We need to educate students about AI and empower them to think critically and ethically about AI adoption, to prepare them for a future in which it will play a significant role.Rohini R. Rao is an associate professor at the department of data science and computer applications, Manipal Institute of Technology, India.If you’d like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

AI-aware pedagogy for business courses

AI-aware pedagogy integrates an understanding of specific AI technology relevant to specific courses

Student engagement is a critical component of academic success and retention in higher education. Engaged students are more likely to participate actively in class discussions, seek out additional learning opportunities and persist through challenges, research has revealed. Incorporating AI into our teaching could enhance student engagement.We’ve found combining tried and tested methods such as gamification and storytelling with AI to be effective. Gamification turns teaching into an interactive experience that promotes personalised learning by offering immediate feedback and fostering collaboration and positive competition among students, and storytelling engages students in a more immersive and emotional experience, facilitating better understanding and retention of content. Here are some tips for integrating AI into the design of gamification and storytelling activities.Identify the theme and learning objectivesEstablish the knowledge or skills that students are expected to acquire or improve as a result of the activity being designed. By being clear about the goals, educators can adjust the activity to meet the diverse learning needs of students and offer additional support when needed. Once you have a clear understanding of the topic and what needs to be reinforced, it’s time to turn to AI for assistance.Design the narrative and/or game elements of your activityUse an AI tool that you are familiar with, such as ChatGPT or Claude. Then, insert some prompts to gather information on your topic. Construct a detailed prompt that includes the theme and the objective of the activity and establishes a variety of questions, difficulty levels and narrative. You can ask the AI tool to summarise key points or generate questions for a quiz or interactive activity.Here’s an example we’ve used: “Build an escape room to reinforce the stages of the water cycle. It must have three levels of difficulty, with five multiple-choice questions each.” You can correct, modify or adapt the results according to your needs.Next, take this content into a gamification platform such as Genially, Canva, Blooket or Quizizz. These platforms allow you to design interactive games, quizzes or visual materials. By combining AI-generated content with the engaging formats of these tools, you can create a dynamic, educational experience for your students.Resources on the use of AI in higher educationThree ways to promote critical engagement with GenAIIs it worth paying for GenAI?AI gamification in practiceIn our Cultural Heritage of Mexico course, we use AI and storytelling to design an activity in which students are assigned the role of a time traveller who must tell a story that combines elements of the intangible cultural heritage and current issues in their country in a narrative format.The student applies their theoretical knowledge to use ChatGPT to generate a relevant story. The idea is to employ the prompts correctly to obtain an appropriate, logical and coherent result. Students must then create an audio story with sounds, dialogues and dramatisations. The results have been incredibly interesting, with stories about Mayan warriors fighting against gentrification, Aztec gods losing “followers”' in the modern world, and pre-Hispanic gods battling against climate change.In the course The Myths that Inhabit Us, we swap exams for gamification exercises. To create these, we asked ChatGPT to generate playful and dynamic activities based on various theoretical concepts that our students had to reinforce. An example of this was an escape room following a story about dragons to reflect Joseph Campbell’s stages of the hero’s journey. Our prompt requested three levels of difficulty, each with three multiple-choice questions. Once we were satisfied with the questions and the AI-generated story, we transferred the content to Genially to build an interactive activity. Then we shared the link with our students.Using curiosity and creativity, educators can harness the power of AI to revolutionise education, preparing students for success in an increasingly dynamic and complex world. These innovative approaches not only promote active participation and deeper understanding but also ignite motivation and curiosity in the educational process.Dania Lorenia Arriola Arteaga is a digital experience professor and Bárbara Regina Granados Guzmán is a professor in the department of educational innovation and digital learning at the Monterrey Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Using AI gamification and storytelling in humanities courses

Combine AI with gamification and storytelling activities to enhance student engagement

Using AI to create engaging educational games for humanities students

The rapid rise of new technologies in this century has meant that not only are we increasingly seeing new ways to collect data, but we are also seeing new things to collect data on.&nbsp;In 2008, the launch of Glassdoor.com gave management researchers and academics data that we’d never seen before. Previously, anonymous reviews and salary data from employees were hard to obtain. As scholars were given the insight to ask new questions or approach existing ones from a fresh angle, organisational research boomed.&nbsp;Equipping students with data skills to navigate an AI-driven worldFinding a community and career through data skillsEnhancing research integrity and impact in APAC using dataMore recently, three major trends have created new opportunities for harnessing new data sources. First, we can now gather valuable data in new ways, using cutting-edge technologies such as wearable health monitors and advanced imaging techniques.&nbsp;Second, the cost of field experiments has fallen and&nbsp;firms and other&nbsp;organisations&nbsp;now often run their own experiments, creating fruitful partnerships between researchers and industry.&nbsp;Third, social media, artificial intelligence, blockchain and sensor-enabled devices are generating vast amounts of “big data” through their digital footprints.&nbsp;However, as opportunities to use new data arise faster than best practices can be established, there are a number of challenges that come with this. To&nbsp;embrace the full potential of this new data while remaining ethical, we need robust practices and recommendations in place for management researchers and other social scientists.That is why, in a recent&nbsp;editorial I wrote with my fellow editors at the Academy of Management Journal, we outlined two key recommendations for management academics using these new data sources for research purposes.&nbsp;Take data context seriously&nbsp;When using new data sources in research, provide detailed context about the data’s origin, compilation and purpose. Unlike more established databases, new datasets require thorough background information to enable accurate interpretation. For example, researchers using video data to study leadership emotions must justify how the emotions in recorded speeches represent broader leadership styles.Similarly, when employing text-mining techniques on corporate filings, it’s crucial to explain who compiled the reports, the motivations behind them and any legal requirements that might influence what is included and what is left out. Conducting targeted interviews or providing contextual information that tells the story of how the data came about can significantly enhance the credibility and relevance of the data. It also instils confidence in the reader that the data accurately represent what the authors claim it does.Contextual clarity also ensures the data’s representativeness and accuracy in reflecting the broader phenomenon being studied. For example, data from public forums may not represent the full breadth of societal discourse on a particular topic. Similarly, when using longitudinal data, be aware that what is and is not included in the dataset might have shifted over time – due to regulatory changes, for example.Management researchers using audio or video data must consider how the recording process, such as in board meetings, might influence behaviours or decisions. Due to the Hawthorne effect, people can change their behaviour when they are aware of being recorded. When we can acknowledge these limitations – and mitigate them as much as possible – we can help to align theoretical arguments with empirical reality.This is particularly important when using machine-learning approaches to coding and other forms of data processing and interpretation, where simplifying assumptions and disregarding contextual factors such as economic shocks or decision-maker constraints and biases can skew results. Providing historical, institutional or geographical context, along with sample data, can offer a clearer understanding of the data’s relevance and integrity.&nbsp;Focus on data transparency and ethical considerations&nbsp;Transparency and ethical concerns are particularly crucial when adopting new data sources. How data were obtained, processed, analysed and stored must be made clear. Management researchers should follow best practices such as pre-registration of study design, ethical review and making anonymised data available for peer review.Data usage must comply with data agreements, ownership rights and consent procedures. When using social media or AI-generated data, authors should address potential risks such as data manipulation or fake accounts. Make detailed disclosures available, possibly through online appendices, to maintain transparency and reproducibility.&nbsp;Additionally, transparency in data handling is critical. For machine-learning methodologies, researchers must show how patterns might vary with different approaches or training datasets to avoid biases. Current Large Language Models, for example, may perform well in specific cultural contexts but poorly in others, highlighting the need to be aware of representational biases.Field experimenters must balance scientific rigour with the potential harm to participants. For instance, designing experimental conditions that carefully control the main factor of interest can create adverse effects for participants or undermine experimental realism. Researchers should clearly define stopping rules for experiments to mitigate potential harm, ensuring ethical considerations are integrated with scientific objectives.&nbsp;Mobilising new data sources presents challenges. But, at the same time, it provides exciting opportunities for advancing management and organisational research. The process often requires new analytical techniques and, due to the increased complexity and additional scrutiny this might invite, can deter some authors. However, using these new data sources will allow us to conduct further innovative and ground-breaking research – so we must learn how to deploy it ethically and effectively.Anne ter Wal is professor of Technology and Innovation Management at Imperial College Business School and is currently serving as an associate editor at the Academy of Management Journal.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

How to handle data ethically as a social scientist

New data sources are generating vast amounts of information for researchers. But social scientists and management researchers should put robust practices in place to remain ethical

How to handle data ethically and effectively as a social scientist 

With nine out of 10 topics already planned in a law module I was updating, I needed to select a topic for the last session as well as edit the content for another session to ensure it was detailed enough but not too complicated. I also needed to formulate hypothetical scenarios for workshops.So I wondered if artificial intelligence (AI) could help with these tasks and speed up the process of updating my constitutional and administrative law module, as well as check my work afterwards. I wasn’t sure which generative AI platform to use, though, and whether it was worth paying for a subscription. I asked some experts at a workshop on Gen AI in the legal classroom, and when they didn’t have an answer for me I decided to find one.The results were…surprising.&nbsp;How does generative AI work?The first question, is how does generative AI (GenAI) work? At a basic level, it is just an advanced version of predictive text. If you’re interested in the science behind it, this Financial Times explainer is less boring than it sounds, I promise.What GenAI models did I test with which prompts?I tested three free and three paid-for Gen AI models:FreePaid-forChatGPT 4ChatGPT 4o&nbsp;($20/£18 p/m)Claude 3.5 SonnetClaude 3.5 Sonnet Pro ($20/£18 p/m)GeminiGemini Advanced (£18.99 p/m)For my prompts, I used the “syllabus co-creator” and “lesson crafter” templates from More Useful Things’ instructor prompts. Each platform received the same basic information about my module, students and planned sessions.&nbsp;This is how many prompts it took each platform to arrive at a useable first draft of the syllabus:
  
      
  
    Image
                



          

  
&nbsp;Test 1: ChatGPT4 and ChatGPT4oThe prompting process for ChatGPT4 was straightforward and intuitive and the outputs accurate. Its activity suggestions were sensible, appropriate and varied, and it was more detailed overall than Claude or Gemini. However, the topic suggestions didn’t fit with the rest of the module and weren’t practical enough in my view. I also found them too niche, and less creative and detailed than some of the other models I tested.Should you pay for ChatGPT 4o?With the free version’s cap on prompts meaning you’ll temporarily be shut out of the platform, I did get more done in less time with the 4o version. I also got access to the image-creating GenAI DALL-E, which enabled me to make pictures for my lecture slides. However, otherwise, the output on 4o was almost indistinguishable from 4.So, while the longer conversations and image creation were useful, I don’t think they’re worth the price tag.Test 2: Claude 3.5 Sonnet and Sonnet ProClaude seems more conversational than ChatGPT, making for a more natural-feeling prompt process, which makes it a good platform for honing your prompting skills. This model also gave me better topic suggestions, one of which will end up in the final version of my module.Resource collection: AI transformers like ChatGPT are here, so what next?Deepfakes are coming for higher education. What can we do to prepare?Mastering generative AI: crafting reusable prompts for effective learning designHowever, despite more data input, the results were less detailed on Claude than on other models I tested. The workshop activity suggestions were not practical enough as well as being repetitive proposals such as Q&amp;As, presentations and quizzes. Additionally, the output formatting was my least favourite – I found it difficult to read from an accessibility standpoint.Should you pay for Claude Pro?My short answer is: no.&nbsp;Again, you’re limited on prompts with the free version, but there wasn’t a marked difference between the free and subscription versions. In fact, it took longer to prompt the pro version.Test 3: Gemini and Gemini AdvancedThe free model was – to put it bluntly – terrible. It struggled to deal with more than one set of instructions at a time, stalled and repeatedly misinterpreted my prompts. I had to spend a long time refining and restating myself to get to something even remotely useful.In the end, I didn’t get as far with this model as the others because I got so frustrated that I gave up.Should you pay for Gemini Advanced?Given how bad the free version was, I nearly didn’t test the advanced version. However – in the name of science – I gave it a go, and I was glad I did because, in a shocking turn of events, it ended up being my favourite.Gemini Advanced was the most perceptive and intuitive platform, making for first drafts that needed less refinement and were more practical. The outputs were also more detailed overall and the lesson plans were creative and included a good range of suggested activities. The topic suggestion was the same one as Claude’s, but I arrived at it more quickly, and it gave me plenty of detail without prompting.This model clearly had the biggest difference between its free and paid versions and is the only one I think is really worth paying for.So should you take out a GenAI subscription?GenAI significantly sped up the process of updating my module. My unscientific estimate is that – over the course of the whole process – it saved me several weeks’ worth of work. This is mostly because it gave me starting points for subject content rather than me having to do extensive research of the possibilities. Instead, I could zero straight in on specific topics. So, for time saving alone, I would recommend using it. I also think that my syllabus and lesson plans are better for having made use of this technology.As for subscriptions, Gemini Advanced was the clear winner for me, and it is tempting to pay for it. I will be taking advantage of my free trial, that’s for sure.&nbsp;So have I convinced you to use GenAI for module updates? Have I sold you a subscription? If so, do you think I can get a commission…serious question.Sophia De Arez Cintra&nbsp;is programme director for postgraduate diploma and first year MSc in law and professional practice and a senior lecturer in professional legal education in the Dickson Poon School of Law at King’s College London.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

How useful is artificial intelligence for syllabus design? A law lecturer compared the free and subscription versions of three generative AI platforms, with surprising results

Is it worth paying for GenAI?

Nearly 20 years ago, Tom Coates, an early adopter of the internet at the BBC, wrote about media companies who were terrified that broadband might kill linear TV. He compared the reaction to their plight to people being pursued by a giant snail. Doom was inching ever closer, yet their feet remained pinned to the floor. Rather than make any movement, there was a lot of shouting. Still the snail advanced. We can all see how that’s turning out for linear TV.&nbsp;When it comes to the digital transformation of higher education, there is widespread agreement that universities must adapt, bringing in the skills and technologies needed to thrive in an era of mass online learning and artificial intelligence. With a handful of notable exceptions, however, the sector seems to find it hard to translate those words into tangible, radical change.&nbsp;How to create a start-up dynamic in academiaHigher education needs a united approach to AIHow small universities can make big digital changesUniversities are institutions where multidisciplinarity, experimentation and optimising for learning are deeply embedded. Yet that isn’t always the case when it comes to the fabric of the organisation itself. There are good reasons for that. The substrates of old IT systems, ropey data and steering committees create friction, a problem far from unique to universities. A cultural gap between academics and professional services can mean a lack of trust on both sides. And higher education also faces a real and growing cybersecurity threat. Building a shining city on a hill when foundations are rocky is not straightforward.&nbsp;Yet however formidable the challenge, the giant snail is not going away. There are practical, tangible ways for universities to make headway and build momentum with their digital transformation.&nbsp;Switch your mindsetDelivering the benefits of digital transformation requires a change of mindset. Organisations built before the internet best navigate this disruption when they fundamentally change how they think about technology.&nbsp;Rather than starting with business requirements, successful examples began by establishing what users – students, staff, alumni – need through data and qualitative research. All universities must meet two different types of need: transactional (“submit an assignment”) and advisory (“prepare for remote learning”).&nbsp;A new mindset goes beyond just thinking differently about services. Instead of building everything upfront and launching to an unsuspecting audience, successful efforts employ a test-and-learn approach. This means starting small, building prototypes, gathering feedback – and repeating that cycle. Instead of the burden falling on the IT team, delivering services is a multidisciplinary endeavour, often requiring the incorporation of new skills into the mix.&nbsp;Rather than layers of governance boards checking in on deliverables, leaders set outcomes, track the data that indicates progress and work to remove blockers to momentum. If an organisation as complex as the government can pull this off during a crisis in teams like the Covid Vaccine Taskforce, so can others.Lead from the frontCreate clear, accountable leadership roles for the services that matter most to users. Responsibility for services often seems either distributed across multiple individuals, or focused at senior executive level far away from day-to-day delivery. Identifying service owners and empowering them to lead improvements makes ownership clearer, speeding up decision-making and turning the focus to what users are asking for, rather than the institution talking to itself.&nbsp;The Open University recently appointed Nick Macarte as the first service owner for their enrolment service. His goal is to radically simplify student enrolment by spotting and removing friction for those going through the process. Early on, he learned the importance of prioritisation, stating: “It’s the first time there’s been a single point of accountability for the enrolment service. We’ve worked hard to set focus and priorities, based on understanding the needs of our students. This allows us to be clear about what changes we will make now and what will come later.”Live the changeAnother crucial trick of digital transformation is to show, not tell. Many will be fed up with promises of technology-enabled change that never seem to amount to much. Winning the argument for this kind of change on paper with abstract arguments and business cases can only take you so far. Pick one or two simple but highly visible exemplar services to change – like “find a bursary” or “attend an open day” –&nbsp;and demonstrate what you mean by new ways of working by living them. Build prototypes. Record people’s reactions. Show stakeholders how much change you can make in a couple of weeks. By doing so, you quickly uncover the longer-term structural changes – to legacy technology, data and processes – the institution needs to secure bigger prizes.&nbsp;Collaborate to innovateNo institution is alone in facing these problems. As Mark Thompson pointed out at the THE Digital Universities conference in April, there is a compelling logic for the sector to work more closely together to address common challenges. Higher education institutions usually require the same components of digital services – systems for taking payment, for example – where there is no competitive advantage to be gained from building one’s own. Collaborating on developing and running these utilities could save institutions time to focus more on what makes them unique, while saving money.&nbsp;Higher education faces a tough time on several fronts. Among them, the giant snail of digital disruption will continue to advance. Best start moving.Andrew Greenway is managing director of&nbsp;Public Digital and an independent member of council at the University of Exeter.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Change is coming, whether higher education likes it or not

Universities may talk about digital transformation, but are yet to translate it into action. Here’s how to start building the momentum for change, instead of standing still 

When hybrid learning and immersive technology can connect and engage students working in any location, where does that leave all those lecture halls and libraries? We ask two US experts about how universities are adapting to the digital transformation

THE podcast: how technology is reshaping the 21st-century campus

It is tempting, faced with breathless hype and apocalyptical scenarios about the impact of Generative AI on higher education, to keep one’s head down and hope it goes away. Alternatively, we might try to exclude its influence, whether through blanket bans and academic misconduct processes, questionable GenAI detection programs or wholesale changes to assessment practices, such as reversion to in-person unseen exams.Neither response seems satisfactory. Clearly, many students are using these tools and will continue to do so, but not always in the ways we expect. Recent surveys suggest that only a minority would consider using GenAI to produce coursework (and perhaps still fewer in humanities disciplines). Much more worrying is the substantial proportion who trust GenAI outputs for basic research, summarising articles and debates and feedback on their work. We should worry less about GenAI’s impact on assessment and much more about its impact on student skills and understanding. Further, students themselves are increasingly anxious about how it could affect their prospects, and are seeking training and guidance.The good news is that a considered response that involves introducing GenAI into our teaching is also probably our most effective means of discouraging students from relying on it. Rather than trying vainly to exclude the influence of GenAI, here are three suggestions to promote critical engagement drawn from a project I’ve been running with history and ancient history undergraduates this year.1. Familiarise yourself&nbsp;It’s an eye-opening experience to try out a programme like ChatGPT, not for a joke task like “Write Macbeth in limericks”, but to try to get a serious account of a topic in your field of expertise: to marvel at both the fluency of the language and the banality of the content, even if there are no obvious inaccuracies or an invented bibliography.There is now plenty of good, clear advice on GenAI in general, but you’ll get a better idea of its capabilities and limitations – in other words, what your students might be drawing from it – by looking at concrete examples of its output. You can easily create your own account on ChatGPT and ask it to write 200 words on a specialised topic, even specifying that it should write as an expert and give supporting references. Comparing the results with your knowledge of the topic, it’s easier to get a sense of how GenAI “averages” relevant material in its database and presents it in an authoritative manner.Given the environmental costs of using GenAI, however, there’s a lot to be said for a collective approach that involves discipline-based sessions for staff, ideally with a few more experienced people present to lead the discussion in which team members analyse and discuss examples of GenAI output relevant to their field. These can be preserved for future reference.Resource collection on GenAIRather than restrict the use of AI, let’s embrace the challenge it offersApply the principles of critical pedagogy to GenAI2. Show rather than tellBlanket assertions about GenAI’s flaws and limitations such as statements in student handbooks and so forth are likely to have limited influence when students are hearing positive and enthusiastic accounts of it from friends, social media and even other parts of the university. It’s more effective to incorporate the analysis of concrete examples into our teaching.For example, a seminar where students are expected to read a key article in advance can include a discussion of an AI (such as Scholarcy) or a GenAI summary of the same publication. This opens up debate about identifying the core argument of the piece and about what is and isn’t essential to its development; it encourages students to compare and justify different interpretations, developing their critical skills. Subtly, it emphasises that reading is about more than the extraction of nuggets of content, but constructive engagement through a focus on the critique of the AI output rather than the limitations of students’ own readings.Similarly, presenting a short GenAI account of a topic in class and asking students to evaluate it encourages the application of broader subject knowledge to specific material. This works well as a revision and consolidation exercise, so is relevant even for those who don’t feel they want or need to engage with AI. It also opens up the opportunity for a wider discussion of the key characteristics and limitations of GenAI output.3. Incorporate GenAI into assessmentIf your module assessment includes some source analysis, it’s easy to include a GenAI passage, labelled as such with the original prompt. Besides emphasising the nature and limitations of such outputs, this is a useful test of subject knowledge and critical analysis. In my pilot, this was a popular option; the moderator noted that “clearly there is utility in developing critical skills – although some of the answers already seem quite rote; they know what is wrong with GenAI and say it quite generically”. There are worse outcomes…For coursework, students might generate a short passage on a relevant topic using GenAI and refine it with multiple prompts, then write a critical analysis of both the output and the process. It’s reasonable to class this as a kind of essay, rather than needing formal approval for changing the mode of assessment. It works as an option for keen students rather than being mandatory. There is no need for special assessment criteria; “knowledge and understanding” and the like are evaluated in terms both of the module subject matter and of GenAI.Many students are currently using GenAI. Our best option is to support them in engaging with it critically and develop their subject skills at the same time.Neville Morley is professor of classics and ancient history at the University of Exeter.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Three ways to promote critical engagement with GenAI

However much we fear its impact or despise its outputs, when teaching humanities, the best response is to encourage students to engage with it critically

The workplace of the near future might see safety trainers using virtual reality (VR) for hazard-recognition exercises, medical professionals employing AI-assisted diagnostic tools or warehouse operations managers coordinating fleets of autonomous robots. Advanced technologies will soon be integral to the way we work.&nbsp;To prepare for this shift, higher education institutions must assess students’ technological familiarity, their readiness to engage with these tools and their purposes for using them. Surveys and user experience can offer insights that will guide the design of instructional environments and inform decisions regarding investments in technological infrastructure, facilities and human resources.Use surveys to assess competencies and determine needsSurveys that gather data from students and instructors can provide a broad understanding of students’ technological competencies and highlight areas that need attention. But who asks what of whom, and when?When surveys are conducted at the institutional level – by, for example, a research centre that reports directly to the provost’s office – these should focus on technologies that will impact everyone, such as generative AI, VR and augmented reality (AR). The survey could include items that explore students’ access to and use of these technologies, their familiarity with features (such as the capacity to build an avatar or teleport within a virtual space) and their confidence in using these features. It should also examine whether and how students use these technologies for learning.&nbsp;Campus podcast: university success stories in managing AI and building digital capacityResource with insight into GenAI’s potential impact on higher educationWhat does it mean for students to be AI-ready?These questions can be adjusted for department-specific surveys to focus on particular tools or programmes relevant to the field, such as Unity for VR environment development, Python or Matlab for data analytics and AI-based models.Instructors have the expertise to curate and integrate the most relevant and cutting-edge technologies into their curricula, so they play a crucial role in the assessment process. However, it is departments that should develop surveys for instructors to gather their observations and predictions regarding students’ readiness to deploy emerging technologies and the need to tailor these tools for specific purposes.&nbsp;These instructor surveys should be conducted at the end of each semester to collect data that will inform instructional settings for the following semester. This data can guide decisions such as IT investments in specific tools, curriculum updates and departmental resource allocation planning.Brief user experience sessionsUnlike surveys that reveal general trends across large groups, brief user experience sessions provide more profound, nuanced insights. These targeted, hands-on sessions allow participants to directly engage with specific technologies, performing tasks within a defined time frame. These sessions generate rich data, including task completion times, methods and strategies used, quality of outcomes, and data from follow-up surveys or interviews focusing on students’ experiences. These sessions, conducted at the department or college level, enhance understanding of students’ interactions with emerging technologies. They offer detailed insights into how these tools function in real-world applications, identifying specific needs, pain points and opportunities for implementing these technologies in academic and professional settings.Conducting these sessions before investing in new technologies and after students have had experience with them is highly beneficial. This dual approach allows for observing and understanding user experiences and reflections on new technologies, which helps identify areas for improvement. It provides a hands-on perspective that surveys alone may not capture, ensuring that integrating new tools into educational practices is as effective as possible.Make technologies availableMaking advanced technologies accessible is crucial for enhancing students’ skills effectively in professional environments. Our research at Georgia Tech’s Center for 21st Century Universities focused on VR and AR readiness among students, and instructors have revealed the significant impact of students’ familiarity with VR devices on their confidence and perceived importance of these technologies.&nbsp;This underscores the importance of institutions making VR headsets and related equipment readily available. A starting point could be implementing a programme to lend VR equipment for students’ personal use. Additionally, offering more courses and workshops where students engage in VR content creation and practical applications will help them grasp the potential of these technologies in educational and professional contexts, fostering positive expectations and enhancing their skills through collaborative learning experiences.By employing comprehensive surveys and hands-on user experience sessions, institutions can gain valuable insights into students’ technological readiness. These data-driven approaches will enable educational leaders to make well-informed decisions on curriculum design, technology investments and resource distribution, effectively equipping the future workforce.&nbsp;Meryem Yilmaz Soylu is research scientist in the Center for 21st Century Universities at Georgia Tech.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

To train tomorrow’s workforce, higher education institutions must assess students’ technological familiarity, their readiness to engage with advanced tools and their purposes for using them. Here are two ways to gather data

How comfortable are your students with AI and VR?

Advancements in new technologies - notably artificial intelligence (AI) – are significantly and rapidly altering the way we work and, in turn, the skills we need. Work practices have evolved both in terms of conditions (our relation to spaces, teams and interactions) and tasks (tools to be mastered, company technological stacks to be integrated into management practices) even this century. And thanks to AI, automation is targeting more and more jobs. In place of traditional quantitative skill sets, AI-leveraging employers are placing a premium on machine learning-related capabilities, as tasks such as data entry, basic data analysis and low-level coding activities are automated. Training savvy and legitimate leaders involves looking ahead of the trends and anticipating the needs for companies.As workforces change, chunks of business schools’ curricula can become outdated and irrelevant. So institutions have a responsibility to adapt their content and teaching to meet the demand of the market and narrow the seemingly ever-growing skills gap.&nbsp;How to train entrepreneurs in five stepsHow business schools could lead the charge to revive faith in higher educationUniversities as business launchpads: creating spaces for enterprise to flourish“But my institution already offers an AI-specific degree programme. What more can we do?” you ask.&nbsp;Here are three things for business schools to consider when optimising AI-related teaching.&nbsp;Experiment with and adapt teaching contentOne of the biggest mistakes a business school can make is not to review or adapt its teaching content regularly. The job requires more than your institution simply offering a specialised MSc in, say, AI or data analytics. Providers must ensure their content is updated to reflect the market and their students. In seeking to improve their courses, schools must embrace experimentation.&nbsp;At ESSEC, in an effort to ensure our teaching is both impactful and insightful, we routinely experiment with our teaching, whether through testing, teaching AI-specific skills and knowledge for alternative industries (such as the arts and law) or allowing students to personalise their learning experiences to best meet their needs. In doing so, teaching becomes an interdisciplinary activity while also being tailor-made for students.&nbsp;Seek out academic and industry partnershipsNone of the above can be achieved without institutions finding the right partners in academia and industry. Cultivating a strong connection with industry is a no-brainer; these organisations (which are also the future employers of your students) can offer real-time feedback on the challenges they face and the skills they need to overcome those obstacles.&nbsp;We constantly experiment with our corporate partners to cover research areas that might affect businesses in the long run. These insights will inform the review and adaptation process. We believe that co-creation is critical and have worked on new positions such as AI model owners&nbsp;for the banking group BNP Paribas and the&nbsp;ethics of AI&nbsp;with the transformation consultancy Onepoint. Our latest initiative, “AI for responsible leadership”, with Accenture, aims at highlighting the competencies and management skills for the leaders of tomorrow.Partnerships with other academic institutions also serve to enrich students’ learning experiences on existing programmes and can expand the breadth of your school’s teaching. Engaging in cross-sector teaching is a key feature that distinguishes a great business school from a good one. Business schools must look to partner with institutions with expertise beyond their own. ESSEC’s collaboration with CentraleSupélec, a leading French engineering school, is a great example of this. Via the BSc in AI, data and management sciences and the MSc in data sciences &amp; business analytics, ESSEC and CentraleSupélec have combined the insights of a business school with those of an engineering institution to offer students, at both undergraduate and postgraduate levels, a breadth (and depth) of learning that, delivered alone, would not be possible.&nbsp;The key is not simply to collate courses but to mesh them completely and create new forms of expertise. This isn’t to say that institutions shouldn’t look to cultivate internal expertise – far from it; to partner with the best academic counterparts, your institution must meet the standard, too. Therefore, attracting faculty that will actively advance your efforts to become a leading provider of AI-specific courses is essential. But to truly optimise teaching, institutions must acknowledge their limitations and know when to leverage external insights.&nbsp;Build student buy-in through ambassadorsWhether experimenting with existing programmes or looking to bolster your offering with new courses, insights from industry or other academic institutions are simply not enough. Schools need the buy-in and participation of their students. They must be led by the feedback they’re given to ensure that teaching reflects the needs and desires of programme participants. Beyond this, however, schools need to cultivate internal ambassadors – champions of the cause they’re working towards. The explosion of generative AI has masked the reality that, often, when educating around new technologies, institutions are dealing with the theoretical; quite simply, they’re educating about issues and concepts that could take place at some point in the future. This requires a willingness from students to engage in such future-facing learning. But, as we’ve seen in the case of AI, all concepts and challenges seem theoretical until they’re not.&nbsp;To get ahead of these issues, schools must prepare students for challenges that are not yet on the horizon – and to do so, they need student engagement. At ESSEC, the student-led thinktank Metalab IDEAS is a space for all emerging questions and topics that might interest future cohorts.&nbsp;The skills gap, as we know, is widening. As the need for digital skills mounts, it’s up to educators to meet that demand. Business schools must look to optimise AI-specific teaching to plug that gap. But there is no quick fix. Institutions must be prepared to adapt, leverage external insights and cultivate student buy-in to address the skills crisis.&nbsp;Abdelmounaim Derraz, Julien Malaurent and Guillaume Chevillon are co-directors of the ESSEC Metalab for Data, Technology &amp; Society at ESSEC Business School, France.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

How to optimise your business school’s AI-related courses

As workforces change, chunks of business schools’ curricula risk becoming outdated and irrelevant. Here are three ways institutions can adapt their teaching to bridge the AI-driven skills gap 

Three ways to optimise your business school’s AI-related offering

I moved into academia after founding a successful software company, so I appreciate the symbiotic relationship between start-ups and universities. Coming from the fast-paced world of software and services, involving international business collaborations, internal HR challenges and team building, I learned that fostering a start-up culture within a university setting can drive innovation and entrepreneurial spirit.Creating a supportive ecosystem is crucial. In the software industry, innovation hubs serve as centres where ideas flourish, resources are abundant and mentorship is readily available. These hubs facilitate the cross-pollination of ideas between local and international business partners, ensuring a global perspective on innovation. Access to funding plays a pivotal role — software companies often thrive through seed funding, venture capital and strategic grants.How to foster a start-up mindset in creative sector studentsResource collection: What innovation needs to thrive in higher educationHow universities can build meaningful collaborations to solve societal challengesWe can translate this model to academia. Establishing education innovation hubs and programme incubators creates dedicated spaces for students, staff and academics to collaborate on groundbreaking educational programmes and provides them with access to necessary resources, mentorship and crucial networking opportunities. Just like in the software industry, these hubs can facilitate interdisciplinary collaboration and share best practices.&nbsp;Build collaborative spacesTo do this, we started by creating a collaborative space on campus where students, staff and faculty can come together to brainstorm and develop new educational programmes. This can be either a physical or virtual space. This space should be well-equipped with the latest technology and designed to foster open communication and teamwork.&nbsp;We then established structured mentorship programmes that connect participants with industry professionals and experienced educators, and organised regular networking events, workshops and seminars, facilitating idea exchange and valuable connections. We often used programme graduates, as well as recommendations from our existing students, as to who should run and facilitate the workshops.Interdisciplinarity for innovationAnother important step is to promote interdisciplinary projects at the executive level of the university. By forming and promoting teams with diverse expertise, great results happen in educational initiatives. This approach, similar to the software industry’s collaboration across different skill sets, can lead to more impactful and creative outcomes. To make this approach grow, implement systems for sharing best practices and lessons learned to continually drive innovation and improvement. Have your teams report out at faculty meetings and to the boards.&nbsp;Encouraging interdisciplinary collaboration is a key cornerstone for innovation. By initiating cross-departmental projects, such as those between criminal justice and marketing, history and computer science, or biology and design, we can unite diverse perspectives that often lead to groundbreaking solutions. These collaborations, while not always obvious at first, can drive the development of innovative educational programmes and research initiatives. To support these efforts, organising interdisciplinary workshops and seminars connects faculty and students from various fields, further sparking creative ideas.&nbsp;Additionally, drawing from my experience in software and services, integrating team-building exercises and addressing internal HR issues within these projects can significantly enhance collaboration and productivity. Together, these strategies foster an environment ripe for transformative educational advancements.Get staff on boardTo involve academic staff, create opportunities that align with their research interests and professional goals. Encourage faculty to participate by offering incentives such as research grants, teaching credits and public recognition for their contributions. Highlight the potential for interdisciplinary projects to enhance their research portfolios and open new avenues for funding and publication. Facilitate networking events and workshops where faculty can meet potential collaborators from different departments.Integrating entrepreneurship into the curriculum, through offering specialised courses and practical experiences such as internships and projects with industry partners, bridges the gap between theoretical knowledge and real-world application. Celebrating successes through recognition programmes and showcase events further nurtures this culture, as does providing technical and administrative support to navigate the often complex path of educational programme development.Take the leapOne of the most valuable lessons from my start-up experience is fostering a culture of risk-taking and learning. Experimentation and failure can drive innovation and resilience, as long as we value the lessons learned in the process. To do this, regular feedback mechanisms can help staff to refine their ideas and approaches.&nbsp;The wider entrepreneurial ecosystem can also provide support here, so form partnerships with industry and accelerators, and leverage alumni networks.Supportive policies and administrative structures within the university are essential. Implementing flexible working arrangements and establishing clear intellectual property policies can encourage staff involvement in entrepreneurial ventures. By integrating these strategies, universities can cultivate an environment where innovation thrives, allowing staff, faculty and students to move quickly, make mistakes and ultimately drive impactful change. This blend of start-up dynamism and academic rigour can propel universities to the forefront of technological and educational advancement.Deborah Sepich is associate professor of marketing and management and director of the MBA programme at CSU Global.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the&nbsp;THE Campus&nbsp;newsletter.

How to create a start-up dynamic in academia

Innovation hubs have sparked ideas, fostered partnerships and raised funding for the software industry. Let’s use the same model to create groundbreaking educational programmes

The AI tech giant OpenAI recently faced a storm of controversy when releasing a voice tool that sounded suspiciously like actor Scarlett Johansson, after she declined to provide the voice herself. Although records now show this wasn’t a deepfake but rather a voice actor hired to provide input, it raises some critical questions regarding how our images and voices may be replicated without our knowledge or permission.&nbsp;What would you say if your university asked to create a digital avatar of you to deliver your lectures? What if this was a condition of your employment? Or what if a student shared a video of you saying something you never said? These are the real questions that we are going to face as this technology becomes more mature and widespread in society.&nbsp;In tackling this topic, the first thing to understand is that there are differences between deepfakes and synthetic media. When we talk about synthetic media, we are referring to images, audio or video which is not of a specific person or institution, but is AI-generated. Deepfakes, on the other hand, are faked media of a real individual.&nbsp;In an artificially intelligent age, frame higher education around a new kind of thinkingHow to prevent cyberbullying from rearing its ugly head in universitiesApply the principles of critical pedagogy to GenAIThe underlying technology is a form of Generative AI (GenAI) which uses Generative Adversarial Networks (GANs) to mimic voice, facial expressions and lip movements. Deepfakes have been around for a while but the technology is now maturing fast. Despite not releasing it for public use, Microsoft recently demonstrated an application called VASA-1, which can create a talking head from a single image and short voice clip, complete with natural head movements and facial expressions. It’s not long before other tech companies catch up – and applications that create less sophisticated deepfakes quickly and simply are now available online.&nbsp;Many governments are making certain forms of deepfakes illegal, including the UK, the EU, South Korea, China, and Canada. However, while legal frameworks are essential for dealing with the risks of this tech, there is currently little focus on what these technologies mean for higher education. These technologies are sneaking up on us while our industry is preoccupied with other concerns. &nbsp;What’s more, we’re providing the material that can be used to create deepfakes of ourselves online. We upload videos, images and audio clips of ourselves to our social networks or through other channels, and this publicly available data may leave us vulnerable to impersonation. &nbsp;In fact, one app we tested required just two minutes of footage to create a deepfake that fooled even close friends and relatives. This isn’t just on the horizon, it’s already here.&nbsp;For educational institutions, deepfakes pose a special set of risks that we need to consider and plan for, both at the level of individual educators and at the policy level. Educators hold crucial roles of responsibility for preparing students for the future and working to safeguard the academic integrity and reputation of our institutions. Deepfakes enable damaging new forms of cyberbullying, academic dishonesty and damage to our credibility.&nbsp;The potential effects of deepfakes on higher educationDeepfakes may be used by bullies to create distressing and humiliating content of students or staff, including non-consensual pornography. Such cases can severely impact young people especially, and the realistic nature of deepfakes makes it difficult to refute their authenticity, thereby exacerbating the victim’s suffering. It has the potential to be far more distressing than traditional cyberbullying.&nbsp;Other effects of deepfakes could also cause problems for schools and universities. Fake recommendation videos from high-profile individuals may begin to plague admissions applications, and deepfake technologies may be used to fabricate research data. This undermines the integrity of educational institutions and devalues the achievements of genuine students. Imagine a scenario where a student submits a deepfake video as a recommendation from a well-known academic or senior professor. The ability to create such convincing fabrications can undermine the credibility of education and lead to inequity.Furthermore, the spread of deepfake misinformation can also severely damage the reputation of a school or university. A single deepfake incident, such as a fabricated video of a university leader making inappropriate remarks, can erode public trust and tarnish the institution's image. In fact, one disgruntled teacher in the US created a deepfake of a school principal, framing them as racist. Incidents such as these could lead to decreased enrolment, loss of funding and a tarnished reputation that takes years to rebuild, as well as sowing social divisions among the students, staff and stakeholders at a school.&nbsp;How to take actionThere are a few things educational institutions need to do to get ready for when an inevitable deepfake incident occurs, which we discuss in more detail in our recent preprint. The first, most straightforward action to take is to update cyberbullying policies to explicitly address the creation and spread of deepfakes. Students and parents need to be made aware of clear definitions, consequences for violations, and the mechanisms in place for reporting and addressing incidents. It’s crucial that these policies are communicated effectively to all students and staff to ensure widespread awareness and understanding.Second, raising awareness about deepfakes is crucial. Educational programmes should focus on teaching students and staff how to recognise AI-generated content and to be critical of any videos they come across. Workshops and training sessions can help build digital literacy and resilience against misinformation, which will prepare them well for the future. For example, integrating media literacy into the curriculum can equip students with the skills needed to critically analyse digital content and identify potential deepfakes.Schools and universities should also develop comprehensive crisis management plans to respond swiftly to deepfake incidents. These plans should include strategies for ongoing crisis communication, communicating with stakeholders and restoring trust. Proactive measures, such as informing potential targets about deepfake risks, can also be beneficial. A university could establish a rapid response team trained to handle deepfake incidents, to provide a coordinated and effective response.Finally, as educators, we need to recognise that while these technologies are risky, there are also potential benefits. Deepfake technology may be used to create immersive learning experiences or enable students to engage in simulated conversations with leading historical figures, or enable the creation of compelling lectures from nothing more than notes and a photo.&nbsp;At the same time, we should adopt a critical-first approach and be prepared for the havoc that could arise from the fact that we can now see anyone doing or saying anything with a few clicks of a button.&nbsp; Balancing innovation with vigilance will be key to ensuring that the benefits of AI and synthetic media enhance rather than undermine the educational experience.Jasper Roe is the head of the department of English and lecturer in social sciences at James Cook University, Singapore, and Mike Perkins is head of the Centre for Research and Innovation at British University Vietnam.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Deepfakes are coming for education. Be prepared

Deepfakes are easier than ever to make, leaving students and academics vulnerable to cyberbullying, reputational damage and educational integrity. Here’s how to be vigilant against them

Deepfakes are coming for higher education. What can we do to prepare?  

Find out how two universities, in Hong Kong and the UK, are embracing generative AI and building institution-wide digital expertise

THE podcast: university success stories in managing AI and building digital capacity

The dawning of the artificial intelligence era could have a transformative impact on academic research. AI can significantly increase productivity by taking over mundane and routinised research tasks in areas such as data processing or analysis.&nbsp;With AI capabilities improving at a rapid rate, it can also now support researchers in more complex and creative aspects of their work, identifying research questions, reviewing prior literature and solving scientific problems.&nbsp;But the next frontier is using AI as a manager of people who perform research. Algorithmic management may be a gamechanger for the organisation and execution of research projects.Resource collection: What innovation needs to thrive in higher educationThe GenAI awakening: navigating the new frontier of research supportThree ways to leverage ChatGPT and other generative AI in researchRecent research we conducted with PhD candidate Maximilian Köehler explored algorithmic management in crowd and citizen science projects. These are projects that involve large numbers of diverse participants with varying skills and motivations. Studying over 60 projects, we identified over a dozen that implemented algorithmic management, demonstrating AI’s effectiveness in five key managerial areas:1. Task allocationLarge organisational goals, such as designing a new drug, often need to be broken down into sub-tasks that can then be assigned to suitable researchers. AI can efficiently match tasks to researchers based on skills, expertise and interests, ensuring optimal task handling. In the project Gravity Spy, AI determines the difficulty of images that need to be classified and then allocates them to participants based on their track record of prior performance.&nbsp;2. DirectionAfter task allocation, managers often need to guide the execution of tasks. AI can assist by offering recommendations, simplifying decision-making and providing real-time feedback and guidance, facilitating timely and accurate task completion. For example, in the project eBird, AI supports participants with automated recommendations to facilitate the recording of birds observed in nature.3. CoordinationCoordination of team activities is essential. Traditional methods like meetings can be time-consuming. AI can streamline this by tracking progress, alerting team members about relevant task completions, and implicitly coordinating actions when assigning tasks, enhancing overall efficiency. We saw this used in the project Aurorasaurus, in which AI coordinated participants so that multiple people could collect complementary data on Northern Lights that appear in a particular place at a particular point in time.4. MotivationMotivating researchers involves more than financial incentives. Intellectual challenges, social impact and peer recognition are significant motivators. AI can tailor motivational strategies by understanding individual motives and measuring performance, providing timely and reliable rewards. The project Galaxy Zoo, for example, has developed AI that can predict when a participant is likely to drop out of the project, sending an encouraging message before it is too late.&nbsp;5. Supporting learningContinuous learning and development are crucial for productivity. AI can customise learning support to individual needs, offering training, tracking performance, identifying problems and assigning challenging tasks to foster skill development. In the project Eterna, for example, AI supports participants who develop new RNA molecules by providing real-time feedback on the performance of their designs.AI’s ability to handle these managerial tasks allows human leaders to focus on strategic and social functions, such as identifying high-value research targets, raising funds and fostering an effective organisational culture. Human creativity, critical thinking and emotional intelligence are still vital for research success – and may become more important than in the past.Of course, algorithmic management raises several concerns. Among others, we need to be careful that algorithms that are able to micro-manage do not restrict worker autonomy, eventually stifling creativity and reducing job satisfaction. The need for personal and behavioural data collection raises privacy concerns. Additionally, there is a risk of algorithmic bias affecting task allocation and reward distribution.To address these concerns, we’ll need awareness, improved technical solutions and robust ethical guidelines and data protection measures. When responsibly integrated into research organisations, however, AI can enhance research effectiveness and enable researchers to focus on their passions and push the frontier of science.Henry Sauermann is professor of strategy and holder of the ESMT chair in entrepreneurship, and Maximilian Köhler is a PhD candidate and research associate, both at ESMT Berlin.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Could AI manage your research project?

The possibilities for using artificial intelligence in research know no bounds – it could even end up in a management role. Here’s what it could do

Social media provides online spaces where individuals and communities can interact and share photos, videos, opinions and interests related to their daily lives, academic or work achievements, and social activities. This makes it a powerful educational tool.Our current students are particularly interested in TikTok, which hosts short videos, covering everything from comedy, news and recipes to tutorials on computer repair. Educators can harness the popularity of TikTok to encourage students to generate and exchange their own ideas and acquire and evaluate new knowledge and skills.&nbsp;Here are four simple ways to use TikTok in your teaching.1. Personal presentationsAt the beginning of the semester, tutors can ask students to record personal presentations, guiding them as to what key information should be included. What is covered in the introductory videos should be aligned to what criteria will be evaluated, such as the student’s interests, their major and their expectations for the course.&nbsp;Once all the videos are published on TikTok, we can ask students to watch their classmates’ videos and comment on those they find interesting or have something in common with, encouraging students to form connections with one another.2. Break topics into short simple segmentsTikTok’s videos address concepts, explanations and demonstrations very concisely. They are ideal for breaking up topics into component parts that can be explained in just one- to three-minute segments.&nbsp;Taking art history as an example, a series of short videos could be created, where each one explains the main characteristics of a different art movement such as the Renaissance, Realism, Impressionism and so on.&nbsp;Tutors could develop a series of videos that summarise all the key concepts in a single unit or module so that these can be reviewed by students in the future.Promoting learning with TikTok: 10 tips for engaging studentsWhat I learned from spending three years researching TikTokResource collection: Social media: how to make it an academic friend not foe3. Reflections of learningStudents can share their reflections on recent course materials or class activities through videos. For example, after discussing a topic in class, students may be asked to provide a personal reflection about their position or the conclusion they would draw regarding the topic discussed.&nbsp;Such videos could also be used as a way to check reading comprehension. Instead of writing a summary, students can be asked to create a video about the assigned reading ahead of class.&nbsp;For example, before a pedagogical theories class, the teacher may share a reading list covering different pedagogical theories such as constructivism, cognitivism, humanism, behaviourism, traditionalism, among others. Before the class, students must read the material and create a TikTok video where they discuss the main characteristics of the theories. These videos could be reviewed at the beginning of class to provide context for further detailed discussion about these theories and how they might be applied.4. Presenting projectsTikTok videos can be used to evidence students’ work such as final projects.&nbsp;Tutors could ask students to create a video pitch relating to their project, meeting set characteristics or evaluation criteria, such as: clear and engaging communication, keeping to a three-minute time limit, crafting a compelling introduction and conclusion, demonstrating how the project works.&nbsp;For example, as part of the final project in a professional communication course, students record a TikTok video pitch in which they have to sell a product they have designed for the course. This provides a valuable learning experience for students but also gives the teacher additional evaluation evidence.An alternative option is a video explaining the process of completing the final project, with similarly specified evaluation criteria, such as the input of all team members, reflections on what worked, what proved challenging and how the team found solutions, and explanations of the initial, middle and final sequences of the project's construction.It is important to decide if your TikTok learning community will be closed, offering privacy to students, or open, allowing wider outreach and engagement. This will depend on the nature of the work or content you plan to share, on your students’ preferences, your teaching profile and your institution.If the content you post is designed for public use, you can keep your community open. However, if the content you present contains intellectual property or personal details that you and your students do not wish to share, keep your community closed. If your students want the content they produce to be available for public use and public display, they can share this on their own public profiles.&nbsp;To build a learning community, we recommend that students follow their classmates and teachers on TikTok.The possibilities for using TikTok in our teaching are endless, so get creative and think about how you can adapt it to your needs.Mariana del Sol González Sierra is a pedagogical architect at Monterrey Institute of Technology.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus&nbsp;newsletter.

Using TikTok as a learning tool

Four pointers for using this platform to enhance the learning process

Engage Gen Z students by integrating TikTok into your teaching

Generative artificial intelligence (AI) has made significant inroads into the higher education sector, with huge potential to transform how educators teach and students learn. From AI-generated lecturers at Hong Kong universities to AI-powered apps that transcribe lecture notes, AI-driven tools are revolutionising educational practices by offering personalised learning experiences and improving engagement. One of the most compelling applications of this technology is in the deployment of AI chatbots to support teaching and learning. But it sparks a crucial question: should institutions rely on generic chatbots or invest in customised solutions?The distinction between generic and customised chatbotsGeneric chatbots such as ChatGPT and Gemini are pre-trained on vast amounts of data and generate human-like responses when answering general questions and fulfilling common requests. While they are good at providing broad information, their responses can be generic, predictable and sometimes mediocre, because chatbots lack the contextual information necessary to tailor their responses to specific educational needs.&nbsp;Apply the principles of critical pedagogy to GenAICan AI offer everyone a personal tutor 24/7?Resource collection: AI and the universityPrompt engineering plays a crucial role in bridging this gap as part of AI literacy training. A well-crafted prompt can significantly improve the chatbot’s response quality. For instance, by introducing a role statement such as “You are an experienced chemistry teacher” and providing contextual information, such as detailed assignment instructions, the generic chatbot can deliver a more customised experience for students struggling with their chemistry homework. Giving the chatbots some examples of the expected output, also known as few-shot prompting, enables AI to produce more desirable output than asking for help without an example (in other words, zero-shot prompting).Customised chatbots take this a step further by using system prompts written by designers, such as teachers, to create an interactive and personalised learning experience. These are different from the prompts written by the end users during a conversation with AI. Instead, these system prompts were written by the designers of the chatbot from the back end to guide the chatbot’s interactions with end users.&nbsp;There are two basic approaches to writing system prompts: branching and sequencing. Through branching, the chatbot would begin the conversation by offering a menu of options. For example, it could offer to illustrate a technique, practice a technique or test a technique, and provide different interactive experiences according to the user selection. Alternatively, through sequencing, the chatbot can offer the end user a structured learning experience, taking a series of pre-defined steps.System prompts also enable the educators to address the concerns of using generic chatbots to support teaching and learning, including hallucination and plagiarism. With information and examples vetted by the teachers in the system prompts, customised chatbots are less likely to produce hallucinated responses than generic chatbots relying on pre-trained textual data. The teachers may also add explicit constraints, asking the chatbot to refuse requests to write full-text drafts, and introduce step-by-step scaffolding to prevent plagiarism.Two pathways to customise a chatbotWhen it comes to customising a chatbot for educational purposes, institutions have two options. Using a well-established platform such as Poe.com minimises development costs because the service provider handles the technical aspects. However, a Poe.com subscription can be up to $20 (£15.68) per month. As such platforms often allow a limited space for system prompts – the maximum system prompt length for poe.com is about 2,200 words – it would be challenging for teachers to write elaborate system prompts to design sophisticated learning experiences. In addition, students’ conversations with chatbots customised in such platforms are private and not convenient to share, making learning analytics challenging.An alternative pathway is to develop an in-house chatbot customisation platform, where teachers could build customised chatbots with a longer system prompt and could access students’ chat history after obtaining their consent. Of course, the up-front costs of developing a platform such as this are significant. Programming expertise and technical know-how are required to send users’ prompts together with system prompts to AI service providers such as OpenAI. This would be undertaken through an application programming interface (API) and with responses returned from the APIs.&nbsp;Yet the long-term costs can be lowered. The AI services could be purchased through API tokens based on the actual usage amount of the university community, rather than per-user monthly fees.&nbsp;Collaboration between teachers and programmersTo build a successful platform for chatbot customisation, it is essential for front-line teachers to collaborate with colleagues with programming skills. At Hong Kong Baptist University (HKBU), such collaborative efforts have already borne fruit. As a lecturer in English from the language centre, I have been working alongside a programmer, a recent MSc graduate in AI, to develop a chatbot customisation platform over the course of 11 months.&nbsp;In a recent seminar on gen AI that attracted nearly 300 participants from more than 70 institutions, we introduced the rationale and features of this platform and received positive feedback and numerous enquiries about trying the platform in the new academic year. The platform has also been used in a summer boot camp on AI that allows secondary school students to develop customised chatbots for promoting STEM education. This collaboration exemplifies how interdisciplinary teamwork can lead to innovative educational tools that better serve students’ needs. Practical tips for customising chatbots in educationEmbarking on the journey of customising chatbots for educational purposes can seem daunting, but with the right approach and resources it can be a rewarding endeavour. Here are a few practical tips to get you started.&nbsp;Document existing practices: Review and document your current teaching practices, especially those involving personalised interactions such as one-to-one tutoring. This documentation will serve as valuable data to customise chatbots that can emulate these interactions effectively, making the AI more relevant and useful for your specific educational needs.Engage with the latest research: Stay updated with the newest studies and findings in the realm of AI and education, especially generative AI customisation.&nbsp;Following and contributing to scholarly discussions, such as&nbsp;the special issue on this topic at STiLE (Scholarship of Teaching in Language Education), can provide insights into the latest trends and successful practices in chatbot customisation. Such engagement also helps in networking with peers who are working on similar projects, providing opportunities for collaboration and innovation.Leverage internal expertise: Reach out to colleagues within your institution who have programming skills and experience with generative AI. Collaborating with them can help you to understand the technical requirements and possibilities of integrating AI through APIs for a more seamless and powerful use.By following these tips, you can embark on the path of creating customised chatbots that enhance educational experiences and outcomes, tailored specifically to the needs of your students and your teaching objectives.Simon Wang is a lecturer in English at the Hong Kong Baptist University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Where to start with generative AI chatbot customisation

AI chatbots could transform teaching and learning, yet generic versions lack the detail needed for education. Here’s how to get started with building your own customised chatbot

With the rise of remote and hybrid education, being able to scan, review and edit documents digitally has become all the more important. But even before the pandemic closed campuses in many countries, BP Mobile (AIBY Group) identified a gap in the market for a reliable scanner app that could optimise internal processes and document management. The iScanner app appeared in Apple’s App Store in 2015, and later on Google Play and for desktop, and has since been downloaded more than 100 million times.&nbsp;The app allows students and higher education staff to scan documents on the go, work from any location and keep all their files on any device. “It allows users to scan, sign, highlight and annotate documents and print files directly from the app,” explains Matt Svetlak, product director at iScanner, BP Mobile (AIBY Group). “They can extract text and share excerpts from a textbook or blackboard notes with the whole class in seconds via Google Classroom, Canvas or any messenger.”&nbsp;Users can scan documents with their phone cameras and complete their tasks from anywhere, instead of having to use an office document scanner. iScanner guarantees a high-resolution scan in seconds, with automatic edge detection, straightener feature and editing abilities. Time-pressed teaching staff in particular benefit from these features as they can scan, edit, sign and share documents in a few seconds. “This frees teachers from time-consuming, routine tasks, allowing them to focus on their students,” Svetlak says.Students can use iScanner in any situation where they want to capture notes, scan multiple pages of books or capture handwritten notes on a whiteboard. There are document-editing tools so study materials can be collated from different sources, annotated and re-ordered depending on what the user needs.AI algorithms have improved the app’s capabilities even further, meaning users can now enhance their documents, removing distortions such as fingerprints and creases that might previously have rendered a scan unusable.New AI features on the app, based on ChatGPT’s application programming interface allow users to translate, summarise and rewrite texts from their scans, explains Svetlak. “Now you can use all the AI tools with any hard copies by simply scanning them first. It helps students and academics save time while doing research and gathering evidence. For students of architecture and interior design, the app offers features that can measure rooms and items, with the option of making a 3D plan in just a few taps.&nbsp;For students and educators, scanning course texts and resource materials is much easier to manage than carrying books and paper assignments. iScanner includes a free cloud storage and syncing feature, so materials can be viewed across multiple devices and even offline. In 2024, the iScanner app introduced a promotion offering six months of free access to iScanner PRO and 10GB of Cloud storage to individuals with a “.edu” email address from educational institutions. Those who don’t have a .edu account can access the offer by submitting a valid student or educator ID. Recognising that sensitive academic documents and data require safe storage, iScanner has built-in security features, such as remote logout if a device goes missing. Users can set up a PIN code to protect their files and be warned if someone else tries to access their documents. They can also set up the app to show decoy documents if an unauthorised person attempts to access their files. “The app is full of safety features that give users full control over who can open their files and what information they may see,” Svetlak says.&nbsp;Download the iScanner app by BP Mobile (AIBY Group).

By providing an easy, reliable and secure way to scan and share documents, the iScanner app contributes to the digitalisation efforts in higher education

The iScanner app supports the academic community in information sharing and management

The integration of generative AI in higher education can offer numerous benefits but it also risks reinforcing existing power structures and marginalising diverse voices. We must critically and ethically engage with these tools to ensure equitable and democratic educational practices.The power dynamics of GenAIAs universities grapple with the rapid integration of generative artificial intelligence (GenAI) technologies, it’s important that we examine how these tools reflect and reinforce existing power structures while marginalising diverse voices. As we continue to use GenAI in teaching, we must reckon with how it defines knowledge availability and influences the perspectives to which our students are exposed.&nbsp;It can be easy to fall into the trap of thinking that GenAI is a neutral technology, yet this definition is much less clear than a purely technical view of GenAI. Latent semantic analysis may be a nominally neutral process but, as yet, it’s not clear how these tools approach encoded bias in data, issue framing and the complexity of ongoing academic debate.How can generative AI intersect with Bloom’s taxonomy?Four steps for integrating generative AI in learning and teachingCollaborating with artificial intelligence? Use your metacognitive skillsThe ethical issues surrounding GenAI in education are profound. The technology’s reliance on surveillance – vast amounts of data often scraped without consent – raises significant concerns about intellectual property and the reinforcement of existing inequalities. These datasets, when traced back to their source, are predominantly from white, patriarchal and Eurocentric perspectives. Even data selection, tool design and training influence GenAI output, and successful performance in these areas is often defined by commercial metrics rather than social interest. The risk is that GenAI will reproduce and even amplify the dominant narratives, undermining efforts to diversify and decolonise the curriculum, and perpetuating the erasure of marginalised communities’ histories and experiences.&nbsp;Overarchingly, research into educational technologies has focused on the artefacts – the tools – as the site of the research, rather than the problems they present. However, the challenge is not merely technological but ideological. The integration of GenAI into educational systems can subtly yet powerfully reinforce notions, such as the neoliberal ideals of efficiency, standardisation and productivity, that are often at odds with the principles of democratic education.&nbsp;If we allow these tools to shape our educational practices without scrutiny, we risk erasing the rich, diverse tapestry of voices that should be at the heart of higher education. With the current range of tools, their data sources and their usage, we may very well be looking at a new wave of curriculum colonisation, if we don’t intervene. We need to be comfortably uncomfortable with our adoption of GenAI, using a critical pedagogy lens to contribute strategies for educators and policymakers, to resist the reinforcement of dominant hegemonic narratives and ideals within educational contexts.Employing critical pedagogyTo engage with GenAI ethically and responsibly, we must accept a level of discomfort. The challenge herein is how we can be comfortable with this discomfort. In other words, how can we acknowledge and recognise the ethical issues of GenAI while leveraging the benefits it can offer? Previously, my co-authors and I have written about the application of the human-in-the-loop framework to GenAI interactions – the idea that a human is involved in evaluating GenAI inputs and outputs around its automated processes. This provides a basis for ethical GenAI interactions, but alone, it’s not enough.&nbsp;To this framework, we must apply the principles of critical pedagogy, such as fostering critical consciousness (an awareness of social, political and economic injustices), promoting dialogue, challenging oppression and encouraging reflective practice. This looks like questioning and challenging the assumptions and biases inherent in these GenAI technologies.&nbsp;To achieve this, educators and policymakers need to be vigilant, asking critical questions each time GenAI is employed in the educational process:How diverse and ethical are the data sources and perspectives that inform the AI’s outputs? Are the voices and experiences of marginalised communities represented and respected when using AI-generated content?How can we involve students in the conversation about the ethical use of GenAI in their education, ensuring their perspectives are included?Are we using GenAI to supplement or supplant critical thinking, supporting, rather than replacing, the unique contributions of educators in curriculum design? Are we prioritising productivity at the expense of obliterating reasoned and critical practice in learning and teaching?How can we ensure that GenAI tools do not perpetuate existing inequalities or create new forms of inequity in our classrooms?How can GenAI tools help educators identify and challenge our own biases in curriculum development?How do we ensure that GenAI co-developed curriculum is transparent and accountable to all stakeholders, including students, educators and communities?Yet, to be able to ask these kinds of questions, educators and policymakers need to develop GenAI digital literacy. By educating ourselves on how to critically evaluate AI tools and understand their limitations and potential biases, we can become more informed and empowered users of GenAI technologies.&nbsp;In practice, educators can encourage ethical GenAI practices by selecting and using GenAI tools that prioritise diversity and equity. This might include choosing platforms that offer transparent algorithms and diverse content, as well as advocating for the development of GenAI tools that reflect a wide range of perspectives and experiences and are informed by consensually sourced data. By doing so, we can ensure that the integration of GenAI in education supports and amplifies diverse voices, fostering a more inclusive and democratic educational landscape – helping to make us uncomfortably comfortable with using GenAI in our teaching and learning praxis.Richard McInnes is a manager of educational design; Simon Nagy is learning designer and Laura Airey is learning designer and project lead, all at the University of Adelaide.&nbsp;If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Apply the principles of critical pedagogy to GenAI

Artificial intelligence can shape our educational practices – but when we allow this to happen unthinkingly, what do we risk losing? Here’s how to stay uncomfortable and ask the critical questions

To broaden the reach of our academic research and inspire a future generation of social scientists, we’ve embarked on a new approach to content-sharing on our social media platforms at the London School of Economics.With a rich collection of research articles in our Research for the World online magazine, we had a great starting point for our project. Using this, we produced a series of short research explainer videos for TikTok and Instagram, covering topics ranging from citizenship investment schemes and cultural proxies to confidence culture and the digital workplace.Here’s what we learned:1. Peer-to-peer engagement builds authenticity and connectionStudent voices are powerful. We progressed on the premise that younger demographics would be more interested in academic research findings if they heard about it from their peers. So we worked with a cohort of engaged student content creators to produce videos that were accessible and visible to younger people on broad-reach social media platforms.2. Insight forms the bedrock of content developmentWe used data analytics to understand the performance of research videos across all social media platforms, focusing on views, impressions/reach, engagement and retention. While the LSE’s global reach and the variety of our research in social, economic and political issues make our video outputs hugely popular, insights showed that retention rates of longer research videos often dipped. This aligned with the increase of mobile users and the better performance of short-form community-oriented videos, evident in reports. Based on these, we decided on a video length between 40 and 60 seconds, which provided a clear time frame for the scripts and directed the editing process.3. Focus on topical contentWith a rich repository of articles about academic research, we were able to be selective when choosing the content that we believed would be of most interest to our audience.Through insights, we know topical issues are most engaging for our audiences on social media, so we identified the articles that explored current affairs from interesting angles and could be produced in a short time frame.4. Managed creative innovation is keyAs we embarked on this new programme of activity, we were aware that we needed to work with creators who were familiar with the requirements of content for our channels and also with our positioning. We opted to work with students who had previously worked with us as part of a larger marketing project.Resource collection: The post-pandemic university: how to serve the Covid generationTips for effectively communicating your research’s impactYou’re a content producer now, part one: how to write articles for a mainstream audienceThe online magazine articles we used as bases for our content had already neatly organised complex research in an accessible format for an external audience, which facilitated their adaptation to video scripts. The students worked alone on adapting them to one-page scripts that concisely presented the research. When the students began production, we recommended that they use green screen filters to illustrate their videos with visual elements to make them more engaging. As many of the social science issues we were trying to convey were not easy or appropriate to film on location, the green screen provided a useful solution.Each video highlighted how the research fitted in the debate and ended with the call to action: “Find out more on LSE’s online magazine Research For The World”.The series’ innovative style with students in the role of presenters of our research not only supports accessibility, but also fosters connections within our community.5. Brand projection and resonance are vitalOur series taps into the institution's key strategic priorities of “Educating for global impact” and offering “Research for the world”. Students are encouraged to engage with interdisciplinary academic debates, and advance their analytical thinking. By enriching our outputs with research videos produced by students, for students, we provide our community with further opportunity for critical engagement in online spaces.Last, it is important to note the indirect marketing value it brings. While all external-facing content can inspire prospective applicants, this series truly demonstrates the brilliant minds of our international community in an authentic, inclusive and accessible manner.Overall, the series proved a tremendous success, both in terms of external engagement and in impact of research communications. We managed to approach a previously untapped target demographic and raise the profile of the magazine on visual-first platforms where promotion had previously remained minimal because the format of the content was incompatible.Lina Filippidi is head of social media at the London School of Economics and Political Science.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

AcademiaTok: how to inspire Generation Z with research

Explaining research in a short social media video is a major green flag for the Covid-19 generation. Here’s how to use existing resources to hit TikTok and Instagram

AcademiaTok: how to inspire Generation Z with academic research

Encouraging people to take part in training and development can be difficult at the best of times, but when you throw the word “digital” into the mix, it can make many want to run for the hills! A lack of time, knowledge of where to begin and a general anxiety that seems to surround the whole subject of anything digital, can make it an uphill struggle for anyone with “digital upskilling” in their job description&nbsp;– let alone when it’s for 40,000 staff and students, as it is for us at the University of Exeter.Our 2030 Digital Strategy states that “we will find ways for people to develop digital skills quickly and apply them to real working situations” and “enable people to get stuff done” so the focus on upskilling the university community is a clear priority.How students’ GenAI skills and reflection affect assignment instructionsOpen educational resources to make lifelong learning accessible to allThe digital shift will have many (positive) side-effectsWhat was obvious to us when we started in our newly created roles 10 months ago&nbsp;was that to achieve our target of upskilling more than 40,000 people, we needed to think differently – no more relying on face-to-face sessions or bespoke webinars. We want our staff and students to be able to access upskilling tools at any time that suits them, so ensuring access to quality asynchronous materials was essential.&nbsp;To make something that would appeal to most people we needed to involve and listen to as many stakeholders as possible to truly understand their wants, needs, challenges and fears. This&nbsp;enables us to co-create solutions with our users for our users. We decided to initially focus on the people on the ground, such as line managers and student support teams, to enable us to understand and guide our thoughts towards the best way to proceed. Over time, we’ve met with&nbsp;more and more stakeholders and are receiving increasing levels of support throughout the institution.What are the fundamentals of our digital skills initiative?&nbsp;“Supportive”, “personalised” and “familiar” are words that have guided our approach from the beginning. From our hundreds of hours of user research and stakeholder engagement, we knew that people wanted to be advised on how they could digitally develop but it had to be in a way that was supportive and personalised to them, and at a time of their choosing – and, critically for the institution, data-driven.&nbsp;By developing our own digital assessment tool, we have been able to co-create the solution with our users, getting feedback as we iterate and ensure it solves our users’ problems and adds value to their education, research, work and lives at Exeter. We’re also able to design the user journey from start to finish, personalising the experience of the tool to our users’ specific needs – something that we just wouldn’t get from an off-the-shelf assessment tool.Fortunately, the digital team at the University of Exeter are already set up to develop products, so we’ve had access to a great team of in-house software developers and designers. This has enabled us to build a working prototype of a bespoke digital skills self-assessment tool, which uses the Jisc building digital capabilities framework. It identifies from individuals what their digital capabilities are and provides them with their own personalised and private dashboard that stores their results – showing them their progression over time.&nbsp;Anonymised results are also held within an institutional dashboard, allowing line managers through to heads of departments and senior leaders to gauge the various levels of digital capability across the institution. All data is anonymised and no teams smaller than six people can be viewed.&nbsp;But this is where our self-assessment tool really gets exciting. Based on the individual’s results, it suggests specific learning resources that are directly contained within our own digital skills library.&nbsp;Sharing is caring when it comes to building resourcesWhat was abundantly clear from the beginning of our journey was that there was a huge amount of high-quality, digitally focused resources that had been made in house, by experts within their fields. By virtue of them being made by peers, these materials also came with the bonus of having that extra-familiar, personalised appeal to Exeter stakeholders. When looking at developing resources for our new digital skills library, we saw no need to start from scratch – and, in fact, many benefits in not doing so.&nbsp;Realising this potential, we have made it our mission at any available opportunity to encourage everyone within the university to share either materials they’ve made or personally used to digitally upskill. Creating a team approach and promoting the “sharing is caring” motto has resulted in us gathering hundreds of high-quality resources. It’s also garnered lots of interest from stakeholders keen to work with us in developing new materials, which can be used for the benefit of everyone, including their own teams and departments.&nbsp;A knock-on effect from taking such a united approach has been a growing sense of vested interest, understanding and positive internal promotion. Our buy-in crusade is already in motion!&nbsp;When will it be ready?&nbsp;The main question we now get when we show our beautiful prototype, is “It looks lovely, but when will it be ready for us to use?”&nbsp;In the digital team at Exeter we use Agile ways of working, so we are beginning with launching a minimum viable product (MVP) of the diagnostic tool to pilot with groups of colleagues across the university. This will enable us to gather feedback and make improvements to the tool, releasing updated iterations over time in readiness to launch to all colleagues and then to other user groups such as students and the wider university community.&nbsp;This approach means that we can provide value to our staff and students faster, and can ensure we are developing a user-led product with insight and feedback at the heart of it, making sure we are solving problems for our users, with our users!&nbsp;What’s also really important to us is that this is not just an initiative that focusses on digitally upskilling people for the workplace, but also supports their personal lives. We can help educate them about e-safety, or enhance their digital well-being, for example.&nbsp;For anyone about to start or already under way with a similar journey, our key suggestions would be:&nbsp;Co-create, involve as many people as possible – listen, reflect, listen again.Be clear on your end goal and ensure there are tangible milestones that stakeholders can see along the way.&nbsp;Be honest and transparent in your approach, create a sense of energy and excitement – change mindsets!&nbsp;Katie Steen and Dave Weller are digital skills leads at the University of Exeter.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

When it comes to digital upskilling, sharing is caring

Launching a digital upskilling product is one thing but getting staff to use it is quite another. Katie Steen and Dave Weller harnessed the power of personalisation to get that all-important buy-in

Personalised learning is the future of higher education. To get there, we must integrate emerging technologies such as artificial intelligence (AI), augmented reality (AR) and virtual reality (VR) into our curricula and the student experience.Imagine a virtual, customisable avatar that is available for students to study with and learn from at their own pace. This personal assistant, based on AI and deep learning, would adapt to each student’s preferences, personality and learning style, becoming more intelligent with each encounter. To interact with the virtual assistant, the student would need only an AR headset that would transport them into an immersive classroom.This is what the future of learning might look like. AI in learning can be: lifelong: learns continuously from the environment, on its own and in real timeremote:&nbsp;provides intelligent tutoring and support to students when and where they need itcollaborative:&nbsp;facilitates interactive discussions among students, so they can work together more effectivelypersonalised:&nbsp;analyses student learning patterns, preferences and strengths to tailor learning content and learning pace, improving engagement and comprehensionadaptive:&nbsp;uses algorithms to evaluate a student’s abilities and identify gaps where they require more supportnarrative:&nbsp;engages students with compelling storytelling that enhances learningequitable:&nbsp;improves accessibility for all students and provides personalised learning that better supports diverse learning needs.Here are three ways I see AI and immersive technologies revolutionising personalised learning:1. Adapting education to each student’s learning pace As an educator in a classroom, you can’t adapt to 20 students’ individual rates of comprehension simultaneously. If you accommodate a slow learner, you bore faster learners. If you go too fast, you lose the slow learner, and they disconnect. AI avatars could allow teachers to approximate one-to-one learning.Campus podcast: the future of XR and immersive learningFind out about AI’s potential to give every student a personal tutor 24/7Resources for using GenAI in teaching and researchBy integrating AR into the curriculum, we could help students to become the creators of their own course and tailor the way they want to learn. Educators would train the avatars with the information they need to become virtual tutors. In the beginning, we might teach 10 avatars that are each dedicated to five students who can reserve time with them when they need assistance, similar to a teaching assistant. Students with similarities could be grouped together and paired with the same avatar. Over time, we can transition to more individualised learning in which each student is assigned their own avatar that adapts to their comprehension rate and learning style. Each time a student interacts with an avatar, it adapts to the student’s learning pace and style. That’s the basis of adaptive robotics – learning from the data to continuously improve and respond to the ever-changing needs of the learner. The AI-enabled assistant may even perceive stress and mood to help improve the student’s mental well-being. Human-robot teaming in which the robot senses and adapts to the human’s well-being, for example, has been studied for many years.In fall 2024, SMU Lyle School of Engineering will be piloting an AR experience for mechanical engineering courses to test this out. Students will use AR tools to take some of their courses so that we can explore the possibilities for improved personalisation.2. Improving memorability with gamification and storytellingStudents are already using AI tools that can generate support such as quizzes, recaps and flash cards to improve their study. They ask chatbots questions related to the class lecture.In the future, these tools will become more sophisticated, designed to incorporate storytelling to make learning more memorable and impactful. Among the types of AI learning, narrative AI is among the most effective methods because&nbsp;studies show the human brain is hardwired to process and retain stories more effectively than isolated data. By placing the student in an immersive environment and making them the main character of the narrative, you can tap into their motivations to compete, progress, discover and learn. Educational and narrative games will allow educators to deliver content that keeps students engaged. 3. Customising everything in the student’s learning environmentEverything in the student’s virtual environment – from the scenery to the appearance of the avatar teaching assistant – can be customised to the student’s preference. By&nbsp;using digital twin technology, students can even invite others into their virtual environment and exist in a common classroom so they’re not alone.Learning resource formats can be tailored to individual style, personality and preference, ensuring students receive content how they prefer – whether it’s video, audio, books, articles or other formats. A developer can work with you to easily convert your content into different multimedia formats. The advantages of this point back to narrative AI and the importance of engagement for memorability, motivation and stimulation. Students will have the opportunity to participate in more immersive and interactive stories in whichever form is most engaging. Customisation and adaptability also create a more inclusive educational environment where every student feels as if their educational needs are met. Ultimately, faculty buy-in for AI and immersive technology is necessary for these powerful tools to become effective for students. Leaders in higher education cannot implement these methods with a top-down approach. The faculty owns the curriculum and must have willingness to shift the culture towards adoption. We will continue to face challenges and questions related to AI and AR in education. But we can’t ignore that these technologies are on the brink of fundamentally reshaping how the future workforce will operate, and we need to be at the forefront of this transformation.Nader Jalili is the Mary and Rich Templeton dean of Lyle School of Engineering at Southern Methodist University.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus&nbsp;newsletter.

How AI and immersive technology will personalise learning

Emerging technologies, including AI and augmented reality, can transform personalised learning and tutoring and enhance the student experience. Here’s how it might happen

Three ways AI and immersive technology will revolutionise personalised learning

Higher education in the UK is going through one of the most challenging periods since the introduction of student fees in 1998. Student-staff ratios are up and student satisfaction is down. Money is tight. Of full-time UK undergraduates, 56 per cent work part-time to make ends meet, according&nbsp;to a June 2024 survey by the Higher Education Policy Institute, which means lectures are being skipped. Staff can also be under the cosh. Two-thirds of university academics were considering leaving the sector&nbsp;partly because of workload, found a 2022&nbsp;report from the University and College Union.&nbsp;Universities want more money, students want to pay less, and academics need space to do research and support students. The government’s coffers are empty. Universities also want students to engage creatively and analytically with their subject. But really stretching students can fall by the wayside when resources are thin, and that is to the detriment of both lecturers and students.&nbsp;So, what to do?&nbsp;The answer for a university that meets the Humboldtian ideal of holistic research and teaching, which offers personalised education and serves students from all over the world, lies, we believe, in generative artificial intelligence (GenAI).&nbsp;Resource collection: AI and the universityCase study: can AI chatbots transform mental health in South African universities?Higher education needs a united approach to AIThe mention of GenAI in universities can trigger a certain amount of angst. It is often viewed as nothing more than a portal to plagiarism and the erosion of standards. And it can be. To make good use of GenAI that scrapes and recombines data from the internet, you need to already have an in-depth understanding of your subject – and probably of several other subjects, too. But, applied correctly, GenAI can transform the student experience in ways similar to access to expert, in-person tutorials.&nbsp;A synthetic teaching assistant, for example, can respond in real time to student queries and probe the student’s knowledge with questions of its own. This is not an app. It is a fully integrated AI tool, embedded in the institution’s system. The&nbsp;learning offered is tailored to expand the knowledge and capabilities of the individual student. One of the first “learning buddies”, Syntea, was developed by IU International University of Applied Sciences in Germany and recently launched in the UK by IU’s UK subsidiary, LIBF.&nbsp;Can an AI tutor match an academic discussion? In the first instance it does, of course, cover off core knowledge. If a student were to ask: “What is a bubble sorting algorithm?” they would be given an answer from the course material. If the student then asked, say, for the difference between bubble tea and a bubble sorting algorithm, the information from the course book would be augmented with information from large language models. Guardrails are needed to ensure that the machine stays on topic. Just as importantly, a human tutor should check every new answer from the AI bot. This both ensures that information is reliable and keeps the training of the system accurate.&nbsp;Delegating repetitive tasks to a bot can leave tutors free to tackle more complex questions. For example, at LIBF, we often find that tutors augment Syntea’s answers with content or explanations from other areas.&nbsp;The burden of exam setting and marking is another load that a synthetic teaching assistant can lighten. To pose questions at the right level of difficulty, the system tracks how much knowledge has been acquired and retained. Over time, exams could become superfluous.&nbsp;But will GenAI be a primrose path to the automation and depersonalisation of third-sector education?&nbsp;No. Society, particularly a democratic society, needs to educate as many of its citizens as possible. The system as it stands is, arguably, not fit for that purpose. Technology will be needed to close the gap. Many academics will remember a time when research involved a pencil, paper and index cards, and people spent whole careers writing a concordance. Now, such “solutions” sound, at best, quaint.&nbsp;Well-constructed AI-powered tutors will change universities and university research much more profoundly than online databases did. That’s because what a university can do will no longer be limited by the capacity of staff to manage basic support, marking, admin and the dozens of repetitive tasks that increasingly make teaching in many universities unfulfilling.&nbsp;Universities tend to have a concept of the ideal student: a school-leaver who can study full-time and has been fully prepared for the course they are on (which is also the right one for them) by advanced exams taken at school. There are no basic knowledge gaps for the lecturer to fill, no need to make allowances for time spent working or caring for others, and no need to consider whether the student should choose a different course.&nbsp;By focusing on the needs of that ideal student, universities are not always serving students or society as well as they might. We would argue that they are also failing to make the best use of the skills and knowledge of their teaching staff. Both of those have serious implications for overall economic productivity and human well-being.&nbsp;GenAI won’t solve all of society’s problems – it will likely create new ones. But, when it is used well, it can make space for university teaching staff to support students – and wider society – by helping to offer a personalised tutor, and a Socratic dialogue, to all who want it.&nbsp;Steve Hill is&nbsp;vice-chancellor and CEO of LIBF.&nbsp;Quintus Stierstorfer is director of synthetic teaching at IU Group.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Generative artificial intelligence can trigger a certain amount of angst, but AI’s potential to support student learning should be explored, write Steve Hill and Quintus Stierstorfer

Can AI offer everyone a personal tutor 24/7?

I used to allow students to use artificial intelligence to help them write their papers. I spent a lot of class time explaining how AI works, how they can use it to help them write if they wish, and how I expect them to document its use if they draw on it. I did this because students are likely going to have to know how to use it, and so we should help them understand how to do so effectively and ethically.&nbsp;But I now think that allowing AI use for student writing assignments is a mistake – at least in lower-level classes. It doesn’t help students learn what I want them to learn through writing papers for class. So, I have banned its use.The ban is simple: students are not allowed to use AI in writing their papers. They cannot use it to generate ideas, to “improve” their writing or (obviously) to write their papers for them.&nbsp;Advice for writing and teaching writingCommon features of LLM-created writingResource collection: AI and assessment in higher educationEliminating AI use is also simple in practice. Students do much of the initial work on their papers in class. They read, in class, short academic works. They identify the conclusions being argued for and the reasons given for them. They then draft by hand (without access to electronic devices) an exegesis of the author’s position. We reconvene to discuss the view, and in another class period students write their initial take on things. They complete the paper outside class, handing in their initial handwritten work to accompany their typed papers.&nbsp;The in-class work is not presented to the students as a way to ensure that they’re not using AI. That would undermine the trust needed between students and instructors for a class to be effective. Rather, this work is done in class, without electronics, to ensure students can work in a distraction-free environment suited for careful and sustained thought.&nbsp;Why handwriting rather than typing? This slows things down and encourages thought. &nbsp;Teaching text understanding without AIJust as the ban is simple, so is the reason for it: I want students to learn how to engage with arguments.&nbsp;To do this, they need to read and understand the author’s conclusion. They need to understand the author’s arguments for that view. They need to think about whether the premises of those arguments support the conclusion – and, if they do not, whether they could be modified to do so or whether the argument needs to be abandoned. If the premises do support the conclusion, they need to think about whether they’d also support a counterintuitive conclusion – and, if so, whether this shows that some of the premises should be rejected.&nbsp;Could students use AI to do some of this? Sure. They could, for example, ask AI to outline the philosopher Peter Singer’s argument for famine relief. AI does a nice job of this. But if students use AI in this way, they won’t develop the skill of working out what the author is arguing for and how they do that. I don’t want students to be able to read a digested summary of a paper. I want them to develop the skill to produce such a summary themselves, identifying the main points of an argument and working out how they support the conclusion.&nbsp;Could they use AI to generate objections to the arguments they read? Of course. AI does a good job of summarising objections to Singer’s view. But I don’t want students to parrot others’ objections. I want them to think of objections themselves.&nbsp;Could AI be useful for them in organising their exegesis of others’ views and their criticisms of them? Yes. But, again, part of what I want my students to learn is precisely what this outsources to the AI: how to organise their thoughts and communicate them effectively.&nbsp;Having students start their essays in class and by hand helps them develop their views and how to convey them effectively. As they work through the process of writing their papers, they often realise that their thoughts aren’t as well developed as they thought. I want them to master these skills.&nbsp;When students resist an AI-free writing processSome students are initially resistant to what they see as a needlessly time-consuming approach to writing. Used to skimming texts and banging out a fast 1,000 words, they dislike having to slow down. And when they slow down, some discover that they are less able to engage with texts than they believed.&nbsp;This identifies weaknesses that students did not know they had. But it has also led to some students becoming frustrated at what they see as pedantry on my part. Isn’t their exegesis “good enough” if it “captures the gist” of what was written? More challenging is that if students can succeed without engaging with the texts they read, what questions does this raise about the value of such engagement? This, by implication, casts doubt on the value of a liberal arts education itself. If students can succeed in their chosen careers without being able to critically engage with texts, it is difficult to justify the claim that it is important that they develop that skill. The prevalence of AI is raising questions not just about its appropriate use in the classroom but about whether some of those classrooms are important at all.&nbsp;Other students, however, have expressed appreciation for this slower, more careful analogue approach. One told me that my classes were the only times in his college career he’d felt “like a scholar”; a senior told me she was proud of having finally grappled with a problem and written something that was entirely hers. Several have told me this approach got them to think about ideas rather than grades.So, goodbye, AI.&nbsp;James Stacey Taylor is a professor of philosophy in the department of philosophy, religion and classical studies in the School of Humanities and Social Sciences at The College of New Jersey.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Students may see handwriting essays in class as a needlessly time-consuming approach to assignments, but I want them to learn how to engage with arguments, develop their own views and convey them effectively, writes James Stacey Taylor

Why I ban AI use for writing assignments 

The widespread availability of AI tools drives a persistent question about what they mean for education. A lot of this conversation over the past two years has focused on writing and computer programming. How do we assess these subjects when students can generate customised, original content using tools available to anyone with an internet connection?This initial fear is a great entry point into discussion about the AI-augmented future of education. Georgia Tech’s new course series, ChatGPT for Educators, and my book, A Teacher’s Guide to Conversational AI, both address assessment design in the age of AI because that task drives the most urgent changes educators must make. However, this is merely a gateway into what it means to prepare our students for the AI-augmented workplace they will enter upon graduation.Like automobiles, computers and the internet, AI has a revolutionary relevance that comes from its capacity to drive demand for skills across a robust spectrum. To categorise how we ought to begin to frame teaching AI skills to our students, I assembled a spectrum of people’s responses to artificial intelligence (see below).
  
      
  
    Image
                



          

  
Far too often, I find the conversation around AI literacy is restricted to the end points of this spectrum. On the far left are coexisters, where AI will be a persistent force in our students’ lives, and we must teach them – as best as we can, given how many unknowns remain – how to exist in the world they are entering. Too often, traditional education prepares students for the worlds into which their schools and universities emerged, not the world they exist in now. Part of AI literacy will be ensuring students are ready to interrogate the content they consume; coexisting with AI will mean understanding how it influences what we see.Similarly, on the far right, we find creators, developers, designers and researchers, people who will see AI as likely to be one of the most significant growth sectors in the coming years. Organisations will be desperate for workers who can build AI tools in order to remain competitive. Fears are rising that the growth of AI will dampen some of these career prospects, but we also know that across human history, technology has created significantly more jobs than it has taken. It is naive to assume that will always remain the case, but in the foreseeable future, there will be enormous work for people with the necessary skills to bring about some of what we see as possible.Higher education needs a united approach to AIEnhance GenAI collaboration for future-proof research supportHow to assess and enhance students’ AI literacyBut in my experience, these end points are too often treated as the only directions to prepare students for the impact of AI. We either prepare students for a world in which AI is an external phenomenon that happens to them, like the weather; or we are prepare them for a small set of future jobs. Not everyone wants to be a computer scientist, a software engineer or a machine learning developer, which is perfectly fine.In between those extremes is an enormous range of AI skills that are not relevant to everyone but apply to far more people than the developers of future AI tools. For all the panic about student use of AI in essay generation, there is enormous potential to teach students to use AI to support organising and communicating their thoughts. Critically, these new tools will allow students to contribute more of their insights – but only if we teach them appropriate usage.Similarly, there will be enormous roles in the future for those with AI literacy to support others’ usage of AI technologies. In my day job, I run Georgia Tech’s online master’s programme in computer science (CS), and one common archetype of students we see are those with no formal CS education but who got into software development or programming because their workplace had a small project that required technical ability. These students picked up these skills independently and, over time, became their organisation’s go-to developer.AI will see a similar arc. Some individuals will develop, mainly through self-study and experimentation, an aptitude with these tools that will pay enormous dividends in how they can support the teams around them. These operators will have a multiplicative role in their organisations, even as they need not develop new AI technologies; they will just need to understand how to select the right AI tool for the appropriate problem.We owe it to our students to prepare them for this full range of AI skills, not merely the end points. The best way to fulfil this responsibility is to acknowledge and examine this new category of tools. More and more tools that students use daily – word processors, email, presentation software, development environments and more – have AI-based features. Practising with these tools is a valuable exercise for students, so we should not prohibit that behaviour. But at the same time, we do not have to just shrug our shoulders and accept however much AI assistance students feel like using.&nbsp;Instead, these interactions should be the foundations of conversations and discussions of these AI tools: how did using an AI tool affect that result? How does it differ from what the student would do alone, both in terms of efficiency and learning outcomes? How does it change how the recipient interprets the student’s work?These conversations are crucial to understanding how AI assistance will be perceived and how it will affect students’ understanding of the content, and they also serve as a powerful gateway into discussing the ethics and morality of using AI tools as a whole.David Joyner is executive director of online education &amp;&nbsp;OMSCS&nbsp;in the College of Computing, principal research associate and&nbsp;inaugural&nbsp;holder of the&nbsp;Zvi Galil PEACE (pervasive equitable access to computing education) chair at Georgia Tech.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

Not everyone wants to be a computer scientist, a software engineer or a machine learning developer. We owe it to our students to prepare them with a full range of AI skills for the world they will graduate into, writes David Joyner 

What does it mean for students to be AI-ready?

A workshop held in partnership with Inspera at the 2024 THE Digital Universities UK event explored educators’ perceptions of AI and what they hope to gain from the technology. The workshop – hosted by Inspera’s Brent Mundy, chief product officer, and Ishan Kolhatkar, global client evangelist – invited academics from UK higher education institutions to discuss strategies for designing effective and engaging assessments in the age of AI.When Inspera considers assessment solutions that can incorporate AI, its focus on is what educators want to achieve in this new era of technological advancement. “We want to hear what you want technology to do because there’s no point in us building things that you’re not going to use,” Kolhatkar said.&nbsp;Inspera takes a pedagogy-first approach and maintains that educators shouldn’t be forced into using technology. The best way for an organisation to learn about the tools that are available and suitable for its unique needs is by inviting firms such as Inspera to have an open conversation about what can be achieved through collaboration, said Kolhatkar. Hands-on experience often changes reticent mindsets, he added, referring to faculty members who were reluctant to incorporate new technological tools into their assessment processes. “When they actually got to try something, they were converted,” he said.The workshop’s participants discussed their perceptions and attitudes to incorporating AI in assessment and learning management. Initial AI sentiment checks during the workshop spanned from “exciting” to “Wild West” and “scary”. Mundy sympathised with the audience, acknowledging that the latest technological developments, such as AI, were unforeseen. “It’s all of these things at the same time. This has been foisted upon us and we are all making sense of it and navigating it together,” he said.&nbsp;Mundy and Kolhatkar gauged the audience’s willingness to incorporate AI into the assessment process and diary management. The participants’ key concern was based on trust. One participant expressed surprise that people were most wary of issues around machine learning more than generative AI.&nbsp;Other applications for AI that were discussed included using AI to assist in grading prose, generating feedback on assessment rubrics or marking criteria, creating questions for a retake candidate based on their previous performance and using AI to analyse data on assessment questions and student responses.Inspera’s approach to AI is based on the empowerment of educators and learners. Mundy said that making sure there is more time for meaningful interactions in teaching, learning and assessment is key. “We need to think about purposeful workflows for the jobs that we do from an academic perspective and how we can enable this much better than we do today,” he said.Inspera strives to assess candidates in a way that optimises student engagement, Kolhathar said. “We have helped institutions around the world make their assessments flexible while retaining integrity.”The speakers:Ishan Kolhatkar, global client evangelist, InsperaBrent Mundy, chief product officer, InsperaFind out more about Inspera.

Educators wish to preserve trust and the human touch at the epicentre of learning amid the ‘scary and exciting’ dawn of AI

Scoping the future of AI in academic assessment

Since the information revolution ignited in the 1980s, with the development of the personal computer and internet, education has been undergoing what could be called a “quiet revolution”. At its heart, this has been an information-driven revolution, which has seen extensive changes to the education and research landscape. It’s been a revolution, in that huge changes have happened over the 40-year period, and it’s been quiet because it’s been driven mainly by a bottom-up evolution of education systems.In education, the quiet revolution has allowed for: teaching models that co-evolve with students, varied delivery models and modalities, new roles for teachers/tutors, student focus and greater personalisation, new curricula and global reach through online education networks and infrastructure. This has widened access to first-in-family students, improved opportunities for students studying overseas and supported global development. In the research context, we have seen capabilities transformed to also provide wider access to research through digital libraries, coupled with the open access and open educational resource movements, which have fuelled a research explosion that has seen, for example, growth in access to more PhD theses, faster-to-market innovations, and advanced-research findings/patents.&nbsp;Ironically, one research area that has been left behind is educational research. Unfortunately, historical bias rests here with both practitioners and researchers in the field, to the extent that in some countries its study is called “scholarship” rather than science. All biases are problematic, but this one has held back funding opportunities and could even be seen as a bulwark against cross-disciplinary and institutional innovation and research. The bias has been self-reinforcing, and underfunding has meant fewer opportunities for longitudinal or substantial scientific studies and gaps in the knowledge base.&nbsp;Resource collection: AI transformers like ChatGPT are here, so what next?With AI at their fingertips, are students still learning?Artificial intelligence – friend or foe? It’s up to academiaOn the plus side, universities have put in place robust pedagogic and technical infrastructures capable of delivering online education and research services and introducing education technology applications. This considerable investment in hardware, software, technical knowledge and training was&nbsp;worth it, in retrospect, particularly in light of the pandemic, making our educational institutions more resilient and capable of delivering 100 per cent online education when it was needed, and significantly improving lost learning globally.&nbsp;However, it has meant uneven sectoral development, different models of delivery and fewer chances for sharing practices. The costs of this digital revolution fell first on the “early adopters” and then on the rest of the sector, which grew localised institutional/departmental learning and teaching capabilities and collaborated with edtech and open educational resources providers one at a time. But this has meant universities took on more costs. If the considerable investment that each institution put in had been better supported by centralised government agencies and research support, a huge amount could have been saved at universities over the 20-year period – about £1 million per university per year in additional staffing costs for learning and open access alone, I estimate. It’s another example of moving more cost to universities, a trend that seems to be accelerating. As an additional challenge, individual institutions have to curate the available scientific research and then find the best ways to implement it.In the 20 years since my first report on blended learning, and in light of recent Higher Education Commission (HEC) recommendations, costs would be reduced and quality improved through better research coordination. To achieve this dual benefit, the addition of a new UKRI research institute in education sciences, as the 10th institute, would allow us to strengthen our science base for education, explore pressing challenges, such as generative and responsible AI, dynamic analytics and the ethics of education, while leveraging our cross-disciplinary and institutional excellence and saving the sector a significant chunk over the next two decades.&nbsp;Undeniably, the research base, as fragmented as it was, turned out to be critical for progress in this area, especially during the global pivot to online. For example, in the UK, we were fortunate to have had two decades to test and evaluate online education, and to deliver sound methodologies and curriculum approaches, helping our valued transnational and international students, as well as benefiting on-campus students. However, many thousands of studies replicated the finding that “no significant difference” existed between physical (face-to-face) and online education – but when a combination of physical and online was studied, a “significant difference” was found, which showed better outcomes for blended than either online- or physical-only delivery. Despite being well referenced, this is still not widely accepted or fully understood by some senior executives.While it has been 20 years since my 2004 report on blended learning for the now-closed&nbsp;Learning and Skills Research Centre, the UK HEC&nbsp;report published in April does acknowledge the need to share practices, structure industry engagement and put in standards, but if our adoption as a community of AI and education is as uneven as our adoption of blended learning, and if we don’t start to bring our findings together, we may well find ourselves in a similar position in 20 years’ time with AI and education. Having spent probably a lot more on AI technology, is it time we took stock and put educational research at the centre of our research landscape rather than eking out an existence? We need to work together quickly in this space, as already the options for education research in AI are drying up.&nbsp;For me the next stage of the “quiet revolution”&nbsp;will be noisy, but if we can put in the shared investment, the sector will benefit and outcomes for students will improve rapidly.&nbsp;Sara de Freitas&nbsp;is visiting professor at the Open University and University of South Wales. She is governor of the board of the University of Sunderland.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

If universities’ response to AI and education is as fractured as the sector’s adoption of blended learning, we may well find ourselves in a similar position in 20 years’ time with duplicated costs and missed research opportunities, writes Sara de Freitas

Higher education needs a united approach to AI

At the 2024 THE Digital Universities UK event, leaders from QA Higher Education participated in two panel sessions that brought together a diverse selection of speakers from academia, digital strategy and careers.One panel, which was hosted by QA Higher Education, discussed methods of harmonising human and digital interventions to transform student employability. “As part of a survey of prospective students held by QA Higher Education last year, increasing salary was both the most common and most important motivation behind further study according to respondents,” explained Joanne Dennison, associate dean for learner skills development at QA Higher Education. “This was closely followed by increasing employability.”“When students are choosing a university, we found some really interesting statistics,” Martin Johnson, head of market insights and portfolio at QA Higher Education, added. “An overwhelming 90 per cent said they looked for staff with industry experience.”Evidently, students expect their university experience to allow them to forge and strengthen industry connections. “This includes through internships, cooperative programmes and research projects that align with student career goals,” said Simona Chraim, a business management student at QA Higher Education in partnership with London Metropolitan University. “They also value resources for skill development.” This is something that Matthew Cooper, programme leader at QA Higher Education was keen to reinforce. “I think the two keywords here are currency and accessibility,” he said.&nbsp;On the technical side, further research indicates that younger people want additional help and support regarding the practicalities of finding and applying for jobs. “One thing we were interested to find is that people were specifically looking for ways that AI could help with this,” said Kathryn Skelton, director at Unthinkable, an agency for the creation of digital experiences that inspire, entertain, teach and enable people to connect. “At the same time, the young people that we spoke to were thinking very holistically about their job opportunities and prospective careers.”“Additional tools are really just the wrappers around the services you’re providing,” said Nikki Drake, chief technology officer at QA Higher Education. “It’s still important that students can articulate the skills they’re learning and express those to an employer.”In a separate panel discussion focusing on immersive block teaching models, the importance of aligning tech and human interventions shone through. These models have taken different forms at institutions, with Susan Orr, pro vice-chancellor for education and equalities at De Montfort University, explaining that her institution took a very proactive approach to programme management.“We looked very carefully at the various strands that underpinned block teaching,” Orr said. “This included strands like student record systems, student regulation and marketing. There was a well thought through change programme.”At Plymouth Marjon University, meanwhile, Debby Cotton, dean of the Doctoral College, explained that the institution is employing a blended approach that involves some block teaching alongside more traditional pedagogical approaches. “A lot is really about curriculum design,” she said. “We did experience some pushback but it’s really about upskilling people in good curriculum design.”“You have to tailor to the audience,” Dennison added. “Whether that means students need block scheduling or want the experience of living away from home for the first time, you have to cater for both.” Whether this means block teaching will be adopted wholesale by the sector, a demand for universities to embed employability within the fabric of the student experience will surely continue to be a factor. It’s all about meeting student demand – that was evident across both panels.&nbsp;The panels:Harmonising human and digital interventions to transform employability&nbsp;Simona Chraim, business management student, QA Higher EducationMatthew Cooper, programme leader, QA Higher EducationJoanne Dennison, associate dean for learner skills development, QA Higher EducationNikki Drake, chief technology officer, QA Higher EducationMartin Johnson, head of market insights and portfolio, QA Higher EducationKathryn Skelton, director, UnthinkableWill immersive block teaching models become the norm in UK universities?Debby Cotton, dean, Doctoral College, Plymouth Marjon UniversityJoanne Dennison, associate dean for learner skills development, QA Higher EducationSusan Orr, pro vice-chancellor for education and equalities, De Montfort UniversityRoscoe Hastings, director of teaching excellence and enhancement, University of ExeterFind out more about QA Higher Education.

Across two panel discussions, experts from the higher education sector discussed how they were adapting to new student demands and shifting pedagogical approaches

Designing courses that prioritise student experience and transform employability

The arrival of generative AI (GenAI) has completely shifted the discussion about humanity and technology, with the stories of tomorrow emerging today. But what does this mean for higher education? Some suggest that AI may become embedded in all operational areas of institutions. It’s certainly not impossible. Others, like Sal Kahn, suggest that the playing field can be not only levelled but potentially improved through Socratic AI uptake. On a broader social and wider institutional level the benefits are attractive, but the challenges are also significant.Pedagogical innovations&nbsp;Students are now turning to GenAI tools to gain insight into questions they previously would have asked in class and to enhance, clarify or sometimes extend their notes. One challenge presented by this is that some students over-rely on these tools, which may affect their learning process. Even training on the ethical and appropriate use of an AI tool may not address their use of it in itself.&nbsp;AI did not disturb assessment – it just made our mistakes visibleRather than restrict the use of AI, let’s embrace the challenge it offersHow students’ GenAI skills and reflection affect assignment instructionsMore concerning is the influence that these tools will have on the student’s cognitive engagement with the subject matter. In some cases, students will no longer make an effort to learn certain skills and instead leave the thinking and writing process up to technology. We therefore need to implement creative and bespoke pedagogical approaches that anticipate students’ use of AI. It’s imperative that this informs teaching and assessment approaches, development of teaching and learning materials and the modification of learning outcomes for specific subject areas.&nbsp;Essentially, the skills expected of students in a world where these tools will become ubiquitous has to be critically evaluated both in the classroom and at an institutional level. We must develop methods that prevent students from delegating their cognitive engagement and the development of their critical thinking skills to technology.The fallible coursework essayLet’s take a practical analytical assessment norm as a starting point. The traditional coursework essay — a stalwart of university courses around the globe due to the capacity it has to assess intended course learning outcomes — is potentially fatally compromised.&nbsp;Traditionally, students write an essay by planning, drafting and refining their work. To do so, they might access and reflect on the suggestions of online writing tools such as Microsoft Word’s inbuilt review features, external websites such as Grammarly, peer feedback and formative input from course tutors. Academics have raised concerns over academic integrity and honesty over the past couple of decades, but plagiarism prevention service Turnitin helped to reassure many.However, the new prospect of a more powerful, free-prompt-inspired super author, available to students at the click of a button, is looming large. What can course designers, programme managers and higher education leaders do?The assessment solutionsDefinitively identifying AI-written work, with no markers apparent and no previous draft data to refer back to, is extremely difficult.&nbsp;AI identifiers such as&nbsp;Quillbot are useful, but not infallible. At times, they pick up on machine-translated work, but they also can miss it. False positives are also possible, with human-written texts being misidentified as AI-generated.&nbsp;One solution is a balance-of-probabilities approach tied into institutional academic integrity, honesty and authorship policies. Giving students appropriate writing skills training, clearly outlining expectations and the outcomes if they’re not met, will work in many cases. But let’s be realistic – not all.&nbsp;Another alternative is to change the assessment parameters. Portfolio-style works take on greater weighting. The emphasis is placed on the process and not simply the end product. If the process is too protracted and fallible because of time, extended exam style or practical experiment conditions could be applied to assess key skills.&nbsp;Taken a step further, institutional resources, such as writing centres and centrally managed IT applications, can also be used that level the playing field even more. In addition, adaptions can be made in assessment cases where extra time or support is required.Of course, educators need to ensure that AI is not holding the pen, especially for module-learning outcomes designed to inform future employers that students have mastered these skills and competencies. Yet students are facing a future in which technologies only being dreamed of today are the backbone of the workforce. We need to design courses specifically for this purpose and measure how well students can work in tandem with these resources. But for now, and for the majority of courses, promoting honesty and integrity is the way forward for a better relationship with AI.Gareth Morris works at the Centre of English Language Education and Bamidele Akinwolemiwa&nbsp;is a researcher and graduate teaching assistant, both at the University of Nottingham Ningbo.If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter.

With AI at their fingertips, are students still learning?

The ubiquity of artificial intelligence may be affecting students’ cognitive development. Gareth Morris and Bamidele Akinwolemiwa consider how to address this

Teesside University’s London campus has an ambitious goal to deliver engaging experiences in a reliable and cost-effective way. The result is an innovative approach to integrating cutting-edge technology solutions into the campus.&nbsp;The campus was built in just one year. “We started with quite literally a blank canvas,” said Andrew Gill, deputy director of IT and Digital Services at Teesside University. “The blank canvas spans the physical space but also the way we wanted to deliver teaching and learning at the campus.”Speaking at a panel held in partnership with CirrusHQ during the 2024 THE&nbsp;Digital Universities UK event, Gill explained the demand for flexible delivery and access. “Students use a lot of their own devices,” he said. “They need a lot of permissions on those devices, and they want the choice and flexibility to be able to work where they want on campus and still access the specialist facilities that they need.”&nbsp;Students using their own devices was critical when designing Teesside’s London campus, Gill explained. “It completely changes the way they interact with the campus,” he said. The Middlesbrough campus’ Teesside University Advance scheme can provide students with an iPad upon arrival, which Gill said is “a real enabler”.&nbsp;Building on this, the London campus offers more choice with a voucher for students to put towards technology purchases. Whatever students choose, computing power is no longer a barrier to access thanks to the use of Amazon Web Services’ (AWS) Amazon AppStream, a fully managed application streaming service that provides users with instant access to their desktop applications from anywhere.Ben Mowbray, end user computing sales lead for the UK public sector at AWS, said that through Amazon AppStream everyone can learn in the same way. “It enables all students, whether they’ve got a Chromebook or a MacBook Pro. They all get the same level of equity. They get the same ubiquitous experiences,” he said.&nbsp;Teesside University calls this access to specialist hardware and software Cloud Labs. This award-winning system is constantly being tweaked to hit the balance between cost efficiency and delivery. Block-based teaching was chosen as the sustainable and scalable solution for the campus, also allowing a shortened feedback cycle. “We’re continually taking the feedback from students in each block and reintegrating it into what we need to do differently, making sure the environments are fit for purpose,” Gill said.Cloud Labs was developed alongside building Teesside’s London campus, Gill said. The commitment to open a campus in 12 months required flexibility, quick decision-making and unlocking the campus workstream. By design, Cloud Labs offered a safety net against the physical campus falling behind. “Having the labs ready and making sure there are teaching environments available in the Cloud as the campus facilities continued to develop was a good derisk on the overall programme,” Gill said.&nbsp;Other benefits included reducing the campus’ carbon impact by giving the students the choice to buy the devices they want, rather than a standard purchase they might not use. From expanding mobile data allowances to buying headphones, the voucher system allows students to get exactly what they need.&nbsp;Stephen Croke, chief revenue officer at CirrusHQ, which is an AWS partner, explained the benefits of incorporating this service into campus design. “The cost of modelling how the Amazon AppStream solution works, coupled with the fact that you can try this out and test in your own environment, it’s almost zero risk,” Croke said.The panel:Stephen Croke, chief revenue officer, CirrusHQAndrew Gill, deputy director of IT and Digital Services, Teesside UniversityBen Mowbray, end user computing sales and GTM lead, UK Public Sector, AWSFind out more about CirrusHQ.

Teesside University has brought about an innovative academic delivery model that enables staff to deliver engaging experiences in a reliable and cost-effective way

From blank canvas to cutting-edge campus in one year

The mental health of university students is a growing concern globally. The numbers in South Africa are a grim testament to the extent of the problem. In a 2023 study of about 70,000 students across 17 universities in the country, 21 per cent reported signs of clinical trauma, while 37.1 per cent reported anxiety symptoms. Another study, in 2019, found 30.6 per cent of students had thoughts of suicide, while 16.6 per cent had made a suicide plan and 2.4 per cent had made an attempt.&nbsp;The Covid-19 pandemic amplified students’ feelings of isolation and uncertainty.The University of Western Cape (UWC) has developed a student mental health and well-being policy that seeks to address the issue in an integrated manner. An implementation plan that accompanies this policy encourages each faculty to proactively tailor action to their students’ mental health needs.&nbsp;Eight ways to improve your digital well-beingTackling the campus mental health crisis has to be a team sportHow to work smarter, not harder in higher educationMultiple factors contribute to a need for South African higher education institutions to address mental health more innovatively. Many students still deal with the stigma of visiting a counselling centre despite measures to maintain confidentiality. The public health system in South Africa offers limited mental health services, which results in countless students grappling with depression, anxiety and other issues. In addition, there is a shortage of mental health professionals, long waiting lists at clinics, and issues with access for students in rural communities and efficiency of the system.&nbsp;AI chatbots for mental health supportOne promising innovation is AI-driven chatbots. They can provide a nonjudgemental, readily available, cost-effective avenue for individuals to access information, support and guidance related to mental health.&nbsp;UWC wanted to be wiser with their mental health offering, and so introduced a mental health chatbot named Wysa. Free text messaging is tailored to an individual’s needs and guides them through clinically proven interactive cognitive behavioural therapy (CBT) exercises. This approach helps users to improve their mental health before symptoms become severe.&nbsp;Not only do AI chatbots increase accessibility to services regardless of the student’s location, they also have the potential to provide life-saving support in critical situations. Their 24-hour availability gives students access to professional support at any time. If needed, the student can connect to a real-time counselling service for additional input, and there is an SOS function connected to the university’s helplines.&nbsp;As the AI Chatbot is a virtual entity, the student can use the technology free from judgement they may experience when discussing their challenges with another human being, a problem in South Africa and worldwide. This adds a layer of safety for the student user.&nbsp;AI chatbots can offer support tailored to the student’s needs and challenges. Natural language processing and machine-learning algorithms are used to formulate a response to the student’s concerns, which creates a feeling of care and support. In addition, a chatbot can provide daily check-ins, tell the student about self-help strategies and coping mechanisms, and be a presence in their life.&nbsp;This wraparound assistance can reduce risk levels. AI chatbots can serve as an early-warning system, identifying mental health issues before they escalate and offering potential for early intervention.&nbsp;More affordable and accessible mental health servicesChatbots are also an economical way to serve an entire university student and staff population, making mental health resources available to a wider audience. Chatbots can yield valuable data for mental health research, identifying trends, symptoms, user behaviour and the possibility of more effective interventions. By measuring what works and what is effective, we at UWC are able to provide the right solution at the right time on a personalised basis, without losing scalability.&nbsp;While an AI application such as Wysa can provide much needed support and guidance to a wider audience, they are not substitutes for mental healthcare professionals. They augment counselling services and can provide skills to students that they can self-learn. At the same time, it frees up time and resources for those for whom more traditional and focused forms of support are needed.Messaging and interventionSince its adoption at UWC in October 2023, 2,368 users have engaged with the Wysa platform, of which 13 per cent are staff members, including academic and support staff. Upon onboarding, users have reported facing challenges related to motivation (10 per cent) and anxiety (10 per cent). A substantial proportion of users, about 47.26 per cent and 59.1 per cent, respectively, exhibited symptoms indicative of depression and anxiety. This underscores the prevalence and severity of mental health issues among the student population. In terms of engagement patterns on the platform, users on average exchanged 51.33 messages per session and spent 28.19 minutes in a single session, indicating a significant level of interaction and investment in using the platform. Moreover, about 47 per cent of users returned for five or more sessions.Importantly, Wysa has demonstrated its effectiveness in crisis intervention, successfully signposting four instances to emergency helplines and 53 instances to appropriate employee assistance programme services. Feedback from users has been overwhelmingly positive, with 91.78 per cent rating Wysa as helpful.&nbsp;The mental health crisis within South African universities is a multifaceted problem that demands a multifaceted solution. AI chatbots offer a beacon of hope, serving as a readily accessible, stigma-reducing, proactive resource to aid students in their time of need. However, these innovations must be deployed thoughtfully, with a commitment to equity, privacy and ethical considerations.Matete Madiba is deputy vice-chancellor for student development and support at the University of the Western Cape, South Africa.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.If you’re having suicidal thoughts or feel you need to talk to someone, a free helpline is available around the clock in the UK on 116123, or you can email jo@samaritans.org. In the US, the National Suicide Prevention Lifeline is 1-800-273-8255. In Australia, the crisis support service Lifeline is 13 11 14. Other international suicide helplines can be found at www.befrienders.org.

How AI chatbots address mental health issues in South Africa

Artificial intelligence can offer promise for student mental health services in Africa. AI-driven chatbots can provide a nonjudgemental, readily available, cost-effective avenue for individuals to access information, support and guidance, writes Matete Madiba

Transforming mental health in South African universities: the promise of AI chatbots

Over the last year and a half, there have been staggering steps forward in the world of artificial intelligence (AI).&nbsp;You’ll be aware of some of them – ChatGPT; DALL·E 2, which generates images, and Whisper, which transcribes speech. Even more recently, we’ve seen Sora – a tool yet to be released to the public, which claims to generate hyper-realistic video footage.&nbsp;There are definitely positives to generative AI – and we shouldn’t ignore them.How students’ GenAI skills and reflection affect assignment instructionsHow can we teach AI literacy skills?Transition from expert to guide by embracing generative AIRight now, it’s being used as a helpful, time-saving tool in many professions. It can quickly build software, create reports and take on time-consuming, tedious duties, freeing up human beings to focus on big-picture tasks.&nbsp;In education, it is reshaping the way we teach. It provides a new way to keep students engaged and find out information. We have to accept that AI isn’t going away – so it’s important we teach students how to use it correctly.&nbsp;AI’s effect on societyDespite its benefits, generative AI is not devoid of challenges. It can be misused to create fake content, such as deepfakes, which can spread false information or damage reputations. Since AI can make realistic text, images and videos, it is being used to shake our trust in reality.&nbsp;There are also very valid concerns about job losses. If AI can be used to generate stock footage and music in seconds, what will that mean for the creative industries? Further to that, if AI is used for this reason, what does that say for the future of creativity?&nbsp;AI and higher educationHowever, though we know it’ll have an impact, we are less certain about the extent at this stage. There is one clear certainty – and that’s the role higher education plays in shaping an AI-fuelled world. While we must teach students to use AI wisely, we must also take the debate surrounding AI ethics into the classroom.&nbsp;From an educator’s perspective, two things strike us immediately. First, how can we foster debates on the morals surrounding AI in the classroom? And second, how do we teach students to recognise it? We believe educators can – and should – design structured discussions where students engage with ethical dilemmas related to AI. This is why we advocate for students to learn about recognising and addressing ethical concerns surrounding AI, while encouraging them to express their opinions and viewpoints.Equally, the benefits of incorporating case studies and real-world examples highlighting ethical challenges in AI development should not be underestimated. These provide students with concrete scenarios to analyse and discuss further.Empowering students with the knowledge and skills to recognise and address ethical concerns surrounding AI will always be essential. As educators, our goal is to facilitate this process, preparing students to become ethical leaders and responsible innovators in the field of artificial intelligence. Therefore, we are certain that it fundamentally matters, and we can pursue lines of questioning, such as: what principles should guide these decisions? Who is accountable if AI is misused? This will prompt them to think critically and make crucial decisions.It’s also important that we help students differentiate the work of AI from the work of people. Research on AI can help tackle concerns surrounding bias, privacy and plagiarism. If our future is to be driven by AI, it’s important that it fits well within our society’s values and standards. Further to this, the higher education sector can help preserve and maintain human creativity – and build the case for the ethical use of AI within industry as well as in wider society.&nbsp;An uncertain futureThe strengths of generative AI indicate that we may have more to gain than we have to lose. We could – very easily – enjoy a future in which tedious, time-consuming tasks are removed from the workplace, freeing up humans to do the work they find fulfilling. In the higher education sector, it could remove time-consuming jobs that hinder research and help make dense academic papers more readable to a general audience – or even to our students.&nbsp;However, it could also very easily be used to cynically remove human beings from the workplace, sacrificing creativity for profit. It could remove the motivation for students to develop a sense of creativity, thoughtfulness and the ability to think critically. The world will need to create clear, ethical rules and regulations to guide the growth of generative AI, and in higher education, we can help shape this. Many already are – but this is world-changing technology, and this debate must be shaped by every discipline. Very few will be unaffected by it.The debate surrounding generative AI is complex, and our future depends on how we handle it. Generative AI could be our best friend or our worst enemy. What it comes down to is the decisions we make. As a sector, we must ensure we lead the way in making sure these decisions are correct. All in all, academia holds the unique opportunity to shape not only the technology itself but the responsible generation that will wield its power.Ibiyemi Omeihe is lecturer in enterprise and Kingsley O. Omeihe is senior lecturer in marketing, both at the University of the West of Scotland.If you would like advice and insight from academics and university staff delivered direct to your inbox each week,&nbsp;sign up for the Campus newsletter.

Artificial intelligence – friend or foe? It’s up to academia

Higher education will be instrumental in determining whether generative AI ends up as a friend or enemy. Ibiyemi Omeihe and Kingsley O. Omeihe outline what universities can do at this crucial tipping point

Digital transformation

Strategic insight and guidance to assist higher education institutions in leveraging the benefits of technology – from artificial intelligence to campus infrastructure

Latest resources

Discover

More from THE

Sign up

Collaborate with THE

About

Legal stuff