The TELSIG Podcast

Today we take a critical look at the promises and pitfalls of using GenAI in educational research. I'm joined by Jasper Roe, Assistant Professor in Digital Pedagogies at Durham University, and Stephen Gow, former Leverhulme Research Fellow at Edinburgh Napier University and lead author of the Student Experiences on Generative AI Project.
We discuss Jasper's recent book, How to Use Generative AI in Educational Research, and Stephen's work on an AI assisted scoping review. We talk through the importance of foundational knowledge, how we talk to students about research, opportunities for classroom-based scholarship, efficiency trade-offs, concerns around AI detection, the need for 'humans in the loop', and other topics.
Key reading
Gow S, Illingworth S (2026), "Dynamic tensions: an AI-assisted critical scoping review of university students' qualitative experiences of GenAI". Artificial Intelligence in Education, Vol. 2 No. 1 pp. 67–89, doi: https://doi.org/10.1108/AIIE-06-2025-0151  
Roe, J. (2025). How to Use Generative AI in Educational Research. Cambridge: CUP. DOI: https://doi.org/10.1017/9781009675338  
Texts referred to in the conversation
Jansson, D.G. & Smith, S.M. (1991) ‘Design fixation’, Design Studies, 12(1), pp. 3–11. doi: 10.1016/0142-694X(91)90003-F.
Perkins M, Roe J (2024), "Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis". F1000Research, Vol. 12 No. 1398, doi: https://doi.org/10.12688/f1000research.142411.2
Pinochet, L.H.C., Miadaira Hamza, K. & Dwivedi, Y.K. (2026) ‘Editorial: Generative AI, human authorship and the transformation of scholarly communication’, Revista de Gestao, 33, pp. 31–40. doi: 10.1108/REGE-04-2026-0058.
Your Undivided Attention (2025) ‘AI and Cancer: Why Superintelligence Won’t Get Us to a Cure’ [Podcast]. Available at: https://podcasts.apple.com/us/podcast/ai-and-cancer-why-superintelligence-wont-get-us-to-a-cure/id1460030305?i=1000764657602 (Accessed: 19 August 2026).
Guest bios
Dr Jasper Roe is a leading scholar in educational technology, artificial intelligence, and academic integrity. He has worked internationally, holding senior leadership positions at universities in Vietnam and Singapore, where he spent over a decade. His mixed-methods research is deeply interdisciplinary, drawing on applied linguistics, corpus and discourse analysis, sociology, and anthropology. Jasper is the author of How to Use Generative AI in Educational Research (Cambridge University Press) and has published over 30 peer-reviewed journal articles in prestigious outlets. He is best known as co-creator of the AI Assessment Scale (AIAS) and is a regularly invited keynote speaker at international conferences and for intergovernmental organisations, including for UNESCO.
Some of his work in news media is available at the below links:Channel News Asia https://www.channelnewsasia.com/author/jasper-roe Times Higher Education Campus https://www.timeshighereducation.com/campus/authors/jasper-roe 
For academic publications please visit his Google Scholar  Connect on Linkedin  
Dr Stephen Gow was the Leverhulme Research Fellow at the Department of Learning and Teaching Enhancement (DLTE) at Edinburgh Napier University. During this role he led the Student Experiences on Generative AI Project (StudentXGenAI), this project carried out the StudentXGenAI Survey with a response rate of over 7,000 students at UK institutions and interviews with students across the UK in addition to integrating GenAI into the research process. He is an expert on academic integrity, assessment and GenAI, and the Chair of the Northern Academic Integrity Forum. He is now associate staff with Department of Education, University of York and available for consultation and research projects related to GenAI in education. He can be contacted at stephen.gow@york.ac.uk or via Linkedin: Stephen Gow | LinkedIn 
Corrections and clarifications
At 44:05, Phil mentions a Linkedin post by Andrew Maynard which describes the production of an academic article from a single-sentence prompt using Claude Opus. Please note: the model used was actually Claude Fable, and the prompt, although short, was more than one sentence. 
Stephen mentions a paper that cited Jasper about declarations negatively impacting people's trust of research - actually, it is not Jasper but a different Rowe. Full reference for this paper is:
Ngwenyama, O. & Rowe, F. (2024) ‘Should we collaborate with AI to conduct literature reviews? Changing epistemic values in a flattening world’, Journal of the Association for Information Systems, 25(1), pp. 37–181. doi: 10.17705/1jais.00869.
Timecodes
00:00 Introduction02:10 Jasper’s background and introduction to his book03:42 Jasper’s Book Focus05:23 Stephen Gow on GenAI in the scoping review10:31 Is GenAI helpful, neutral or a hindrance?14:29 How has Jasper’s approach to ed research changed?19:05 Impacts on the way we teach research22:03 The importance of foundational knowledge26:53 GenAI for teacher researchers28:38 Efficiency in literature reviews30:20 Limits of auto reviews31:40 The productivity paradox34:33 Efficiency tradeoffs35:47 The case for slow scholarship41:21 Transparent peer reviews44:06 The futility of detection48:54 Preprints and open feedback50:19 Will we still need humans in the loop?58:55 Future research plans

I’m joined by Dr Sophia Mavridi to talk about her doctoral research: Second language students’ experiences of AI-assisted writing in higher education: A phenomenographic perspective. Sophia identifies six broad categories that emerge when observing students’ use of AI assisted writing, and in this podcast we dive into these categories: achieving better grades, expressing ideas more clearly, managing time/workload, habituating academic writing norms (e.g., criticality/reflective writing), increasing interaction, and increasing sophistication. 
We go over inferences and implications of GenAI in student writing, how talk to students about developing a voice, and how we do this ourselves in our own writing, the tension between making your writing distinctive while adhering to academic norms, where we should set expectations for undergrads versus PhD students. Plus: AI slop on Linkedin.
We also cover Sophia’s paper published last year in Technology in Language Teaching & Learning that critiques institutional responses to GenAI in education including prohibition, avoidance, teacher support, technological enthusiasm, and critical engagement. We discuss directions for language teacher education, including rethinking writing pedagogy, discernment, transparency, teacher educator development, and ethical dilemmas.
Speaker Bio
Dr Sophia Mavridi is a Senior Lecturer in Educational Technology and TESOL at De Montfort University (UK). She specialises in digital pedagogy, online learning, and AI in education. She has extensive experience in language teacher education, working with organisations such as the British Council, NILE, and Bell Foundation, and is committed to bridging research and practice.
https://sophiamavridi.com/ 
Further reading
Watch Sophia’s keynote form the TELSIG symposium earlier this year: Behind the scenes: How students use AI (and what we can learn from it).
The survey Sophia alludes to at approximately 55 minutes in is the HEPI Student Generative AI Survey.
Mavridi, S. (2025). Hype, Fear, and Everything in Between: A Critical Typology of Responses to AI and their Implications for Language Teacher Education. Technology in Language Teaching & Learning, 7(2), 103219 Available at: https://doi.org/10.29140/tltl.v7n2.103219 
Timecodes
00:00 Podcast intro 
01:41 Reflections on the PhD Viva
04:55 Teacher identity
08:19 Overview of the PhD research
09:26 Six AI writing categories
11:03 Integrity and voice
14:33 Participants and context
16:55 Self regulating AI use in writing
19:33 Students surprising sophistication
24:45 Struggle  in L2 writing
32:12 Why writing still matters
38:19 Academic norms versus authorial voice
44:11 AI and the loss of voice
49:09 Institutional AI typology
51:51 Teacher education directions
57:05 AI detectors and in-class writing
01:07:30 Tech hype vs pedagogy
01:11:53 Reading AI research critically

All of us started our careers somewhere, and today I'm talking to two old friends and former colleagues about how our years teaching English as a foreign language shaped our view of education and the world. We talk about the resilience and adaptability that those early classroom days taught us, the overwrought lesson plans, learning humility from failure, and how these experiences inform our work now in higher education management, instructional design and ed-tech. 
We go on to discuss the direction of education, the resurgence of interest in humanities subjects in the face of AI, staying up to date with knowledge and skills in an ever-changing jobs market, and the general chaos of those unique times in our lives.
Guest Bios
Ben Naismith, PhD is a Staff Assessment Scientist at Duolingo where he works on research and development of the Duolingo English Test. Prior to joining Duolingo, Ben worked extensively in the field of English language teaching in numerous contexts as a teacher, teacher trainer, materials developer, assessment specialist, and researcher. His current work and research into the assessment of L2 English proficiency focuses on vocabulary assessment and automated scoring of speaking and writing. His work has been published in journals including Language Testing, Studies in Second Language Acquisition, Language Teaching Research, Language Learning, and Assessing Writing, amongst others. He is the lead editor of the forthcoming Routledge Handbook of Digital Language Assessment: Innovations and Insights from the Duolingo English Test.
Andrea Flores is Senior Instructional Designer at MIT Sloan School of Management and Founding member of the Boston AI2030 Chapter. She’s an AI lead and human-centered design practitioner with a diverse background in education, public health, workforce development, and technology. She has worked across non-profits, higher education, and global health, and focuses on human impact and sustainable solutions. 

I'm joined by Peter Davidson from Zayed University to discuss the 2023 article by David Brooks: In the Age of AI, Major in Being Human, which claims: "AI will force us humans to double down on those talents and skills that only humans possess. The most important thing about Al may be that it shows us what it can't do, and so reveals who we are and what we have to offer."
What might these ‘human’ skills and attributes be that we will need in the age of AI? In this podcast we will try to identify these human skills and attributes (what might be termed ‘capacities’) that have become more essential to us as humans, as AI becomes embedded in teaching and learning, and in the workplace. It is these human capacities, Peter argues, that will become increasingly important for our us and our students as the impact of AI grows.
See the full list of Essential Human Skills.
Guest bio
Peter Davidson teaches Business Communication and Technical Communication at Zayed University in Dubai, having previously taught in New Zealand, Japan, the UK, and Turkey. He is currently interested in exploring how Generative AI is impacting language teaching and assessment practices, and how it can be leveraged to improve the educational experiences of students.
Peter is presenting at the upcoming BALEAP PIM at Leeds on June 19th.
Further Reading
The framing of autonomy, mastery and purpose that's referred to in the discussion was popularised by Daniel Pink in his 2012 book Drive: the surprising truth about what motivates us. This draws on the work of Richard Ryan and Edward Deci, and Self-Determination Theory.
Anderson, D.J., Rainie, L, & Anderson, J. (2026). Human Wisdom for the Age of AI: A Field Guide to Cultivating Essential Skills. Elon University and AAC&U.
Anderson, J. & Rainie, L. (2025). Being Human in 2035: How Are We Changing in the Age of AI?Imagining the Digital Future Center.
Gerlich, M. A. (2025). AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking. Societies, 15(1), 6.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf
Pink, D. H. (2012). Drive: the surprising truth about what motivates us. Edinburgh: Canongate.
Postman, N., & Weingartner, C. (1969). Teaching as a Subversive Activity. New York: Delacorte Press.
Raman, A. (2024). Investing in Human Skills in the Age of AI. LinkedinLearning, California, USA.
Ryan, R and Deci, E. (2000). Self-Determination Theory and the Facilitation of Intrinsic Motivation,
Social Development, and Well-Being. American Psychological Association. 55 (1). Available at: DOI: 10.1037110003-066X.55.1.68
 
 

Last year HEPI reported 95% of students were using gen AI, but recent research from Stephen Gow and Sam Illingworth casts doubt on this figure. Today I’m joined by Stephen to talk through some of the key finding of his Leverhume Trust funded study that draws data from over 7,000 participants. What do students really think about gen AI in higher education, and how should this shape the way we treat it in the curriculum?
Guest Bio
Dr Stephen Gow was the Leverhulme Research Fellow at the Department of Learning and Teaching Enhancement (DLTE) at Edinburgh Napier University. During this role he led the Student Experiences on Generative AI Project (StudentXGenAI), this project carried out the StudentXGenAI Survey with a response rate of over 7000 students at UK institutions and interviews with students across the UK in addition to integrating GenAI into the research process. He is an expert on academic integrity, assessment and GenAI, and the Chair of the Northern Academic Integrity Forum. He is now associate staff with Department of Education, University of York and available for consultation and research projects related to GenAI in education. He can be contacted at stephen.gow@york.ac.uk or via Linkedin: Stephen Gow | LinkedIn 
Further reading
Chung, J., Henderson, M., Slade, C., Liang, Y., Pepperell, N., Corbin, T., Walton, J., Yu, AS., Bearman, M., Buckingham Shum, S., Fawns, T., McCluskey, T., McLean, J., Oberg, G., Seligmann, A., Shibani, A., Bakharia, A., Lim, LA., Matthews, KE. (2026). The use and usefulness of GenAI in higher education: Student experience and perspectives. Computers and Education Open, Available at: doi:  10.1016/j.caeo.2026.100347.
Gow S, Illingworth S (2026), "Dynamic tensions: an AI-assisted critical scoping review of university students' qualitative experiences of GenAI". Artificial Intelligence in Education, Vol. 2 No. 1 pp. 67–89, Available at: doi: 10.1108/AIIE-06-2025-0151 
Gow, S. and Illingworth, S. (2026) “It is a temptation to get it to do the work…” – student experiences of GenAI in UK universities. 09 Apr 2026. Advance HE. [Online]. Available at: https://www.advance-he.ac.uk/news-and-views/it-temptation-get-it-do-work-student-experiences-genai-uk-universities [Accessed 20 April 2026].
The Castlereagh Statement is available at: https://castlereagh.ai/
Timecodes
00:00 Welcome and guest intro
01:12 Duolingo streak talk
06:20 Tech backlash and attention
10:46 Generative AI literacy risks
19:23 Introducing StudentXGenAI
22:31 Survey design and access
24:54 Who uses GenAI and why
27:23 Productivity versus learning
31:42 Massification and student pressures
34:26 Research goals and policy impact
34:48 Survey design choices
35:52 UK vs Australia findings
36:47 Why usage rates differ
38:15 Regulation and risk
39:07 Learning tool doubts
41:11 Assessment scales explained
45:42 Trust and honesty data
49:44 Fairness and incentives
56:55 Exams after COVID
01:03:59 Data privacy and costs
01:07:31 Future research

Language students using machine translation has certainly raised lots of questions for those of us teaching English for Academic Purposes over the past few years. But most of the conversation has been around its impact on written compositions. A new study by Lamont and Cirocki looks at how and why it's changing the way international students interact verbally with each other and their teachers. 
We're joined today by James Lamont, the lead author of the study, to dig into the data and talk about the implications for the language classroom. What steps do teachers need to take to enable learning to actually take place?
Speaker bios
Jiaoyue Chen is an Academic Practice Adviser at the University of York, where she supports colleagues’ professional journey through the PGCAP programme, York Professional and Academic Development scheme recognition, and the York SoTL network. With a background in Applied Linguistics, she worked as a Lecturer in English Language and Education at Huazhong University of Science and Technology in China. She still returns to this area of research with great interest, but also seeks to disentangle the nuanced relationship between SoTL and formal pedagogical research to better support student learning. 
James Lamont is an Associate Lecturer at the University of York in the Department of Education and the School of Business and Society, where he supports student skills development. His research interests are student use of technology and developing working relationships across student cohorts. 
Further reading
Lamont, J., & Cirocki, A. (2025). Talking to algorithms, not students: Students’ and lecturers’ perceptions of machine translation in academic discussion. The JALT CALL Journal, 21(3), 103256. https://doi.org/10.29140/jaltcall.v21n3.103256 
Timecodes
00:00 Intro to MT in the classroom01:19 James Lamont and Jiaoyue Chen03:08 Talking to algorithms 04:58 Groves and Mund’s previous work on MT04:58 Real time translation in class07:36 Language acquisition concerns12:19 Tasks versus learning goals16:15 The impact of MT on non-language learning20:42 Overreliance and false confidence26:00 Accuracy culture and dependency29:48 Policy gaps and overreliance31:04 Setting classroom expectations32:57 Phone boundaries and culture34:15 Structured tech use phases35:23 Proficiency gaps and support38:06 Accents, idioms and listening load43:24 Anxiety comfort and safe seminars48:50 Privacy, recording and shame51:48 Student buy-in and agency54:56 Ideal classroom and future research58:03 Final Takeaways And Paper Credit

Are smartphones and laptops enabling or impeding students’ progress in class? On the plus side they give access to a wealth of resources, but they can also kill interaction and provide any number of distractions. Today we dig into the research on devices in class with educational psychologist Paul Kirschner.
Paul also clears up the confusion around cognitive offloading, what it really means and what’s actually happening when we use AI. Is it really just another tool like a calculator?
We talk about these and a range of other learning tech topics, including future research directions for multimedia assessment, and what we can reasonably ask of practitioner research.
Check out Paul's Substack via the link below, and the posts for today's conversation on phones in the classroom and cognitive offloading vs outsourcing.
https://substack.com/@paulkirschner173727 
Guest bio
Paul Kirschner is one of the most influential voices in the national and international education debate. For decades, he has done research on and has been translating scientific insights about learning, memory and teaching into clear applications for education.
Paul is professor emeritus at the Open University of the Netherlands, honorary doctor (Doctor Honoris Causa) at the University of Oulu (Finland), visiting professor at the Thomas More University of Applied Sciences in Flanders and owner of the educational consultancy kirschner-ED. Previously, he worked as a teacher of Science, Chemistry and Mathematics in secondary education and was active in school boards and participation councils of both secondary and secondary education.
He is regarded worldwide as a leading expert in his field and has published approximately 450 scientific articles, in addition to several hundred popular science contributions and blogs for teachers and school leaders. In addition, he is the first or co-author of several influential and widely read books, including Instructional Illusions, How Learning Happens, How Teaching Happens, Evidence-Informed Learning Design, Ten Steps to Complex Learning, Developing Curriculum for Deep Thinking and Urban Legends about Learning and Education.
Further reading 
Sungu, A., Choudhury, P. K., & Bjerre-Nielsen, A. (2025). Removing phones from classrooms improves academic performance. Available at SSRN: ssrn.com/abstract=5370727 or dx.doi.org/10.2139/ssrn.5370727

Phil is joined by Lily Abadal and Nidhi Sachdeva to talk about reducing device reliance, rebuilding in-class writing, and using technology with clear pedagogical intent. Lily describes redesigning written assessments by breaking the traditional term paper into smaller in-class, long-form writing components, encouraging device-free classroom culture without heavy policing, and emphasizing silence, reflection, discussion, and mentorship. 
Nidhi brings research from cognitive science to bear on tech-related concerns like distraction, cognitive load, and outsourcing thinking. She guides us through the limitations of flipped learning, and why we might want to bring some COVID legacy independent tasks back into the classroom. 
We also lay out the stall for why personalised feedback, workbooks and visible teacher investment in students are things worth hanging on to.
Speaker bios
Lily Abadal is an Assistant Professor of Instruction in the Philosophy Department at the University of South Florida - St. Petersburg. She specializes in normative ethics, applied ethics, moral psychology, and philosophy of psychology. Her recent interests include moral injury, character formation, and AI Ethics. She explores all things through a Neo-Aristotelian lens. 
She’s interested in helping mission-centered schools design pedagogical strategies, develop integrity-centered policies, re-imagine assessments that align with their values, and encourage genuine character formation in the age of AI.
Lily writes about all of the above on her Substack, Wisdom in the Machine Age: https://substack.com/@wisdominthemachineage 
You can also find more information on her website: https://www.drlilyabadal.com/
Nidhi Sachdeva is a leading Canadian Science of Learning researcher, specializing in evidence-informed learning design, post-secondary education, and educational technology. She teaches online learning and microlearning from a cognitive science perspective at OISE’s Department of Curriculum, Teaching, and Learning at the University of Toronto. A recognized expert in translating educational research into practical classroom strategies, she has been featured on numerous podcasts and currently serves as Chair of researchED Toronto.
Check out Nidhi’s Science of Learning Substack. Listen to Nidhi’s previous TELSIG podcast appearance on education myth busting. 
Further reading
Abadal, L.M. (2025) Only the Humanities can save the university from AI. [Online]. Public Discourse. Available at:  https://www.thepublicdiscourse.com/2025/07/98429/ [Accessed 23 January 2026]. 
Kirschner, P. (2025), When phones go out the window, learning comes in the door. [Online]. Krischnered. Available at: http://www.kirschnered.nl/2025/11/01/when-phones-go-out-the-window-learning-comes-in-the-door/ [Accessed 23 January 2026].
Oakley, B., Johnston, M. Chen, K, Jung, E. and Sejnowski, T.  (2025). The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI. [Preprint]. Available at: https://arxiv.org/abs/2506.11015
Timecodes
00:00 Intro 02:34 Lily’s background: ChatGPT forces a rethink of assessment04:08 Rebuilding the term paper: in-class slow writing and device-free culture08:29 Nidhi’s stance: thoughtful EdTech (not a tech war)12:30 Offloading vs outsourcing: what cognitive science says about AI/tech15:45 What is the classroom for now? Mentorship, practice, and attention18:29 Lily’s new class design: handouts, recall, annotation, discussion30:03 Lessons learned from flipped teaching35:40 The practicalities of unplugging in Higher Ed37:21 Lily’s case against ChatGPT in Philosophy44:46 Distinguishing EdTech from AI and social media53:48 In-class writing as an alternative to exams55:04 Workbooks and human feedback01:02:02 Beyond essays: low-Stakes Mastery Quizzes & Assessment for Learning01:03:25 Why Handwriting Works: Engagement, Cognitive Science & Iterating as a Teacher

Deanne, James and I gather around a virtual Yuletide fireplace, roast chestnuts and perform that time-honoured festive tradition of chewing over key moments in learning tech and EAP from the year gone by. Much as the shepherds probably did.
Is a full in-class digital detox a good idea, and is this a weird thing to suggest in a technology enhanced learning podcast? Did we ever figure out whether students real-time subtitling us is a problem? Would any of us pay for AI-generated music? Did we get carried away with flipped learning after COVID? 
As we look back on the debates that have lit up 2025, we'd like to wish all our listeners an awesome holiday and a happy new year.  
Further reading
Listen to Klaus Mundt and Michael Groves on TELSIG
Eaton, S. E. (2025). Global Trends in Education: Artificial Intelligence, Postplagiarism, and Future‑focused Learning for 2025 and Beyond – 2024–2025 Werklund Distinguished Research Lecture. International Journal for Educational Integrity, 21(12). https://link.springer.com/content/pdf/10.1007/s40979-025-00187-6.pdf 
Flenady, G., & Sparrow, R. (2025). Cut the bullshit: why GenAI systems are neither collaborators nor tutors. Teaching in Higher Education, 1–10. https://doi.org/10.1080/13562517.2025.2497263 
Kirschner, P., (2025), When phones go out the window, learning comes in the door. Krischnered. Available at: http://www.kirschnered.nl/2025/11/01/when-phones-go-out-the-window-learning-comes-in-the-door/  
Plate, D., & Hutson, J. (2025). The intellectual bankruptcy of anti-AI academic alarmism: a rebuttal. Teaching in Higher Education, 1–12. https://doi.org/10.1080/13562517.2025.2562594 
Timecodes
00:00 Intro to the guests02:41 James’ new paper on student use of translation10:24 The case for digital detox14:03 Pedagogy leads16:41 Phil’s phones away experiment19:55 Has flipped learning failed?26:03 Do students still need English?29:31 Do unsupervised assessments provide evidence of learning?34:50 The AI bullshit paper38:04 Plug for the TELSIG symposium39:54 Would you pay for AI music?46:47 Reverting to what makes for good learning51:35 TELSIG’s Christmas message
Guest bios
James Lamont is an Associate Lecturer in Skills Development, Department of Education, University of York in the United Kingdom. His research interests include the effects of generative AI on student thought processes and outputs, and how universities can adapt to this new environment.
Deanne Cobb-Zygadlo has been an EAP tutor at Nazarbayev University since 2015. She is the  co-coordinator of the Technology-Enhanced Learning Special Interest Group (TELSIG) with BALEAP, which is the accreditation organization for the NU Foundation Year Program. She is also a member of the ENAI (European Network for Academic Integrity) Policies Working Group.
 
 

I’m joined today by Mike Perkins to talk about the AI Assessment Scale, following the publication of the latest version of the scale that appeared in the Journal of University Teaching and Learning Practice in September. 
The AI Assessment Scale has been used by more than 350 institutions globally, has been translated into 30 languages, and is recognised by regulators such as TEQSA (Tertiary Education Quality and Standards Agency) in Australia.
Mike Perkins and co-authors Jasper Roe, Leon Furze and Jason MacVaugh have been recognised as guiding lights for educators around the world responding to the widespread availability of Gen AI tools. 
Mike and I talk about how the team’s thinking has changed on some of the topics related to AI and assessment, their responses to some of the critiques of the original scale, comparisons with other models of AI integration, the international response to the AIAS, and other topics.
References
Perkins, M., Roe, J., & Furze, L. (2025). Reimagining the Artificial Intelligence Assessment Scale (AIAS): A refined framework for educational assessment. Journal of University Teaching and Learning Practice, 22(7). https://doi.org/10.53761/rrm4y757  
Perkins, M., Roe, J., & Furze, L. (2025). How (not) to use the AI Assessment Scale. Journal of Applied Learning and Teaching, 8(2). https://doi.org/10.37074/jalt.2025.8.2.15 
Guest bio
Assoc. Prof. Dr. Mike Perkins serves as Head of the Centre for Research & Innovation at British University Vietnam (BUV). With a PhD in Management from the University of York, his research journey has evolved from studying performance management in local policing to becoming a leading voice in the integration of Generative AI (GenAI) in higher education. Dr. Perkins is renowned for developing the AI Assessment Scale (AIAS), translated into 30 languages and implemented across more than 350 schools and universities worldwide. His work addresses the critical intersection of technology, academic integrity, and ethical implementation of AI in educational settings. He leads research in the equitable application of GenAI, and provides guidance to educators and policymakers responding to the challenges of the new GenAI landscape. 
Dr. Perkins' expertise has established him as a sought-after advisor to educational institutions globally, supporting them in ethically integrating Generative AI to enhance student learning while preserving academic integrity. Beyond his work with AI, Dr. Perkins has conducted significant research on broader academic integrity issues, including investigations into diploma mills and student behavior during the COVID-19 pandemic's shift to online learning. His expertise spans performance management, academic integrity, and the strategic integration of emerging technologies in educational settings.
Check out the AI Assessment Scale website for the most up to date information and resources on https://aiassessmentscale.com/ 
Follow Mike on Linkedin: https://www.linkedin.com/in/mgperkins/ 
Further reading
Corbin, T., Dawson, P. and Liu, D. (2025). Talk is cheap: why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 1–11. Available at: https://doi.org/10.1080/02602938.2025.2503964 
Newton, P. M. and Draper, M. J. (2025) ‘Widespread use of summative online unsupervised remote (SOUR) examinations in UK higher education: ethical and quality assurance implications’, Quality in Higher Education, 31(1), pp. 127–141. doi: https://doi.org/10.1080/13538322.2025.2521174
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence AssessmentScale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. Journal of University Teaching and Learning Practice, 21(06), Article 06. https://doi.org/10.53761/q3azde36  
Timecodes
00:00 Introduction03:42 Mike’s background in AI and assessment06:24 Links to EAP08:12 Differences in the Australian and UK post COVID responses to assessment12:03 How the thinking behind the new AIAS has changed15:20 What are we learning with gen AI?17:44 Examples of AI in teaching and assessment21:00 Assessment for and of learning26:57 AIAS and the two-lane approach29:57 Discursive versus structural changes36:00 Should training be mandatory?38:52 Future directions44:48 What makes a successful writing team?