Opening AI for Language Learning

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 p...

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...

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’...

What does it mean to understand a language? Today’s generative AI tools can produce remarkably convincing linguistic forms, but can these systems understand the language they produce? In today’s episode, Mat and Phil explore John Searle’s Chinese Room thought experiment and its argument that a system can produce appropriate linguistic forms without understanding their meaning. What can language le...

What can generative AI do for language teaching and learning—and where does it fall short? Mat and Phil beam us back to Oxford, Ohio, for Part 2 of their conversations from the 2026 CALICO Conference, exploring AI for language learning, language teacher education, AI-generated feedback, and conversational AI through four perspectives. Dorothy Chun , Professor Emerita of Education and Applied Lingu...

What are researchers learning about the role of generative AI in language teaching and learning? Recorded live at the 2026 CALICO Conference, Mat and Phil interview Robert Godwin-Jones, Jeffrey Maloney, Mingjun Tang, Kimberly Vinall, and Emily Hellmich about AI for language learning, teacher education, digital literacies, pragmatics, and professional development. The guests discuss both the opport...

Mat and Phil reflect on what their own Sustained Integrated Professional Development around GenAI looks like in practice. They discuss books, podcasts, courses, tools, and the conversations that are shaping their own learning as they navigate challenges around time, motivation, curiosity, and staying grounded in fast times. They also share what bothers, intimidates, and overwhelms them when they h...

What does it really mean for a machine to "think"? In this episode, Mat and Phil explore the life and legacy of pioneering mathematician Alan Turing, whose work laid the foundation for modern computing and artificial intelligence. From the origins of the Turing Test to whether ChatGPT has truly passed it, the hosts unpack how Turing's ideas provide a valuable framework for thinking critically abou...

Exploring two contrasting perspectives on AI in education by Andreas Horn and Emily Bender, Mat and Phil reflect on AI’s promises and pitfalls, what artificial intelligence really means, and how language educators can respond thoughtfully to a technology that is already shaping education. In this episode: Both texts are available at https://unesdoc.unesco.org/ark:/48223/pf0000395236 (page numbers...

Mat and Phil welcome their first-ever guest, Glenn Stockwell, to discuss professional development, learner training, and the ethical, legal, and policy challenges of generative AI in language education. The conversation also explores AI literacy, academic publishing, the Gartner Hype Cycle, and emerging research on AI's impact on language learning. In this episode: Glenn Stockwell: https://www.edu...

In this episode, Phil reports to Mat on his adventures at the TESOL 2026 Convention in Salt Lake City, March 24-27. Phil talked with exhibitors and attended as many AI-focused presentations, panels, and posters as he could to get a sense of how AI, especially GenAI, is being presented by and to professional English language teachers. Mat comments on the value of panels and the conversations at con...

In this episode, Mat and Phil discuss a book chapter by Glenn Stockwell, in which he writes about professional development and learner training for AI, highlighting the importance of ongoing reflection, learning training, and documenting professional growth. Glenn is the editor in chief of the journal Computer Assisted Language Learning and a professor of Applied Linguistics at the Education Unive...

Language teachers can build their AI competence through the seven guiding principles of Sustained Integrated Professional Development. From starting small to taking a learner’s perspective, from reflecting critically to working with students and peers, as AI continues to evolve. In this episode: Principles of GenAI-SIPD by Phil Hubbard and Mat Schulze: https://www.igi-global.com/gateway/article/fu...

Mat and Phil return with six additional knowledge areas and skills that language teachers need to navigate AI thoughtfully, including course and lesson creation, assessment, and student misconduct, highlighting the importance of teacher awareness, transparency, and professional judgment in a rapidly evolving landscape. In this episode: Hubbard and Schulze (2025). AI and the future of language teac...

Learning about GenAI? Mat and Phil talk about the first four (of ten) knowledge areas and skills language teachers should aim to have now: prompting, ethics, chatbots, and translation. Combining practical examples with theoretical insights, they discuss these knowledge areas and skills in more detail than they could give in their paper: AI and the future of language teaching: Motivating Sustained...

What is a large language model (LLM), actually? How do these systems work? Why can they feel like human conversation partners, and why is that perception misleading? Mat and Phil open the podcast by answering these questions, and discussing the key implications for language teaching and learning. In this episode: Phil Hubbard: https://web.stanford.edu/~efs/phil/ Mat Schulze: https://PantaRhei.pres...