The TELSIG Podcast

Professor Phil Newton from the University of Swansea talks through his recent paper: Evidence of Learning Through Assessment. Protecting the Value of a Degree in the Age of AI.  Phil highlights clear and present threats to higher education by the sector’s continued use of online, unsupervised and remote assessments. This, Phil argues, is compromising universities’ ability to properly certify learning, and has the potential to erode the value of a degree.  In this conversation we talk through the six key recommendations put forward in the paper, and how they might realistically be implemented.  We discuss where change can come from, the problem of SOUR assessments, the misaligned incentives in higher education, the loss of confidence from employers, the ethical concerns around placing the burden of trust on to students, how other countries have reacted, the need for training, resource barriers, the information that should be included on degree transcripts, and many other topics.  Guest bio Phil Newton teaches Neuroscience across a variety of programmes in the School. He also teaches on evidence-based education as part of the Schools online MSc in Medical Education. He came to Swansea in 2009 from the University of California at San Francisco, having completed a Ph.D in Biochemistry and Molecular Biology at the University of Leeds (2001) and a B.Sc in Biology at Aberystwyth University. He also holds an M.Sc in Medical Education from Cardiff University (2012). LinkeIn https://www.linkedin.com/in/prof-phil-newton-21966b8a/  Web - https://www.swansea.ac.uk/staff/p.newton/   YouTube - https://www.youtube.com/user/newtonsneurosci   Scholar - https://scholar.google.co.uk/citations?hl=en&user=77aJ0IMAAAAJ&view_op=list_works   ORCID - https://orcid.org/0000-0002-5272-7979   Researchgate - https://www.researchgate.net/profile/Phil_Newton/   Further reading Newton, P. (2026). Evidence of Learning Through Assessment. Protecting the Value of a Degree in the Age of AI. Policy Exchange. [Online]. Available at: https://policyexchange.org.uk/publication/evidence-of-learning-through-assessment/.  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 

What does a reconfigured language learning ecology look like? My guest today, Professor Pascual Pérez-Paredes, argues that AI-driven environments can “expand opportunities for language learning while creating risks related to opacity and over-reliance”.  We discuss the continued importance of studying real, attested language use through well-designed corpora, enabling evidence-based comparisons and supporting traceable, reproducible research. This, Pérez-Paredes claims, is more important than ever when unreliable, non-evidenced answers from chat bots are just a mouse click away.  We also discuss corpora in teaching, how AI can make corpus tools more user friendly, costs and local models, and the need for critical AI literacy, open-box pedagogies, and transparency amid increasing synthetic language. Further reading Pérez-Paredes, P., Curry, N. and Aguado Jiménez, P. (2025) 'Integrating critical corpus and AI literacies in applied linguistics: a mixed-methods study', Computer Assisted Language Learning, pp. 1–27. doi: 10.1080/09588221.2025.2569351. Pérez-Paredes, P. (2026) 'Corpus linguistics and AI in the reconfiguration of language learning ecologies', Language Teaching, pp. 1–13. doi: 10.1017/S0261444825101109  Guest bio Pascual Pérez-Paredes is a Professor of Applied Linguistics at Universidad de Murcia, and former Lecturer in Research in Second Language Education at the University of Cambridge. His main research interests are the use of corpus linguistics methods in applied linguistics, corpora and digital resources in language education, learner language variation, and corpus-assisted discourse analysis. He was the Overall Coordinator of the MEd Research Methods Strand at the Faculty of Education, University of Cambridge (2016-2019). His most recent books are Corpus Linguistics for Language Learning Research, John Benjamins Research Methods in Applied Linguistics series with co-authors Geraldine Mark & Anne O’Keeffe; Data-driven Learning in and out of the Language Classroom, Cambridge University Press, co-authored with Prof Alex Boulton and //Corpus Linguistics for education. A guide for research// in the Routledge Corpus Linguistics series. He is currently PI of the ERASMUS+ project “Freedom of movement at play: EU citizens’ identity and transnational discourses” (2023-2025) funded by the European Commission and PI in the Spanish Research Agency -funded project Broadening the scope of Data-driven learning: a multi-site & multi-data approach. Twitter @perezparedes and Bluesky @perez-paredes.bsky.social Google Scholar Profile Research Gate Profile ORCID: https://orcid.org/0000-0002-2796-338X  SCOPUS: https://www.scopus.com/authid/detail.uri?authorId=26038713600#  WOS: https://www.webofscience.com/wos/author/record/G-6496-2011

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 [email protected] 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 Introduction 02:10 Jasper’s background and introduction to his book 03:42 Jasper’s Book Focus 05:23 Stephen Gow on GenAI in the scoping review 10:31 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 research 22:03 The importance of foundational knowledge 26:53 GenAI for teacher researchers 28:38 Efficiency in literature reviews 30:20 Limits of auto reviews 31:40 The productivity paradox 34:33 Efficiency tradeoffs 35:47 The case for slow scholarship 41:21 Transparent peer reviews 44:06 The futility of detection 48:54 Preprints and open feedback 50:19 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 [email protected] 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 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  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 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