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.

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/

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

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.

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.

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.

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?

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

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/

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?

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.

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

00:00 Intro to MT in the classroom 01:19 James Lamont and Jiaoyue Chen 03:08 Talking to algorithms 04:58 Groves and Mund’s previous work on MT 04:58 Real time translation in class 07:36 Language acquisition concerns 12:19 Tasks versus learning goals 16:15 The impact of MT on non-language learning 20:42 Overreliance and false confidence 26:00 Accuracy culture and dependency 29:48 Policy gaps and overreliance 31:04 Setting classroom expectations 32:57 Phone boundaries and culture 34:15 Structured tech use phases 35:23 Proficiency gaps and support 38:06 Accents, idioms and listening load 43:24 Anxiety comfort and safe seminars 48:50 Privacy, recording and shame 51:48 Student buy-in and agency 54:56 Ideal classroom and future research 58:03 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

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.

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.

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.

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

00:00 Intro 02:34 Lily’s background: ChatGPT forces a rethink of assessment 04:08 Rebuilding the term paper: in-class slow writing and device-free culture 08:29 Nidhi’s stance: thoughtful EdTech (not a tech war) 12:30 Offloading vs outsourcing: what cognitive science says about AI/tech 15:45 What is the classroom for now? Mentorship, practice, and attention 18:29 Lily’s new class design: handouts, recall, annotation, discussion 30:03 Lessons learned from flipped teaching 35:40 The practicalities of unplugging in Higher Ed 37:21 Lily’s case against ChatGPT in Philosophy 44:46 Distinguishing EdTech from AI and social media 53:48 In-class writing as an alternative to exams 55:04 Workbooks and human feedback 01:02:02 Beyond essays: low-Stakes Mastery Quizzes & Assessment for Learning 01:03:25 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.

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

00:00 Intro to the guests 02:41 James’ new paper on student use of translation 10:24 The case for digital detox 14:03 Pedagogy leads 16:41 Phil’s phones away experiment 19:55 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 paper 38:04 Plug for the TELSIG symposium 39:54 Would you pay for AI music? 46:47 Reverting to what makes for good learning 51:35 TELSIG’s Christmas message

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.