When should you use AI, and when should you leave it alone? In today’s episode, we dig into the central question of Sam Illingworth’s Slow AI, exploring what it means to move beyond AI tools and techniques and toward using AI in ways that are genuinely worthwhile.

Considering a variety of language teaching practices and processes—from adapting texts to different reading levels to creating lesson plans or grading student work—Mat and Phil discuss the importance of judging when a task can truly benefit from AI and when AI removes something valuable, whether it’s human judgment, or processes that are themselves essential to learning.

In this episode:

Sam Illingworth’s Slow AI: Knowing When to Use AI and When to Leave It Alone https://samillingworth.com/book

Mat and Phil look back to look forward: What can the history of language teaching teach us about its future with AI? If generative AI represents something fundamentally new, does responding to it require leaving behind what language educators already know?

Drawing on Mat’s forthcoming chapter on language teacher education before generative AI, our hosts reflect on what established knowledge about language, learning, and pedagogy can contribute to the decisions educators are making about AI today. How does AI challenge existing practices in teaching and assessment? What does it mean for the importance of language awareness? And, perhaps most importantly, how can educators decide when to rely on technology and when to rely on human expertise?

Making the case for treating generative AI as genuinely new without leaving behind what we already know, the conversation highlights the linguistic, pedagogical, and human foundations that can help (teacher) educators navigate what comes next.

In this episode:

Mat is blogging about his chapter for the Cambridge Handbook of Artificial Intelligence and Language Teacher Education. Go to https://PantaRhei.press and search for the posts with “Beyond AI” in the title.

Hubbard, Philip and Mathias Schulze (2025) AI and the future of language teaching – Motivating sustained integrated professional development (SIPD). International Journal of Computer Assisted Language Learning and Teaching 15.1., 1–17. DOI:10.4018/IJCALLT.378304 https://www.igi-global.com/gateway/article/full-text-html/378304

How can language teachers keep up with AI when the technology itself keeps changing? And how can teacher educators prepare future teachers for a landscape that may look very different just a few years from now?

Drawing on Phil’s forthcoming chapter on Sustained Integrated Professional Development, Mat and Phil explore whether learning about AI needs to become an ongoing part of teacher educators’ professional practice.

Do teacher educators need to be AI experts before they can prepare future teachers? What can educators learn by experimenting with AI themselves? In this episode, our hosts make the case for approaching AI not as something educators can learn once and master, but as an evolving area of professional learning—one that teachers and teacher educators can navigate together, from experiencing AI as a language learner and collaborating with colleagues to setting manageable goals, experimenting with new tools, and reflecting on successes and failures.

In this episode:

Contact Phil by email, if you would like to read a draft version of this chapter for the Cambridge Handbook of Artificial Intelligence and Language Teacher Education. (efs@stanford.edu)

Hubbard, Philip and Mathias Schulze (2025) AI and the future of language teaching – Motivating sustained integrated professional development (SIPD). International Journal of Computer Assisted Language Learning and Teaching 15.1., 1–17. DOI:10.4018/IJCALLT.378304 https://www.igi-global.com/gateway/article/full-text-html/378304