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