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

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 learners gain from interacting with a machine that simulates language but does not understand it? Can learners engage in languaging—constructing meaning, knowledge, and experience through language and participating in human communities—by simply interacting with a chatbot?

Our hosts explore these questions while also considering the opportunities AI chatbots offer language learners, from conversation practice and individualized language exposure to incidental language learning. Along the way, they reflect on what these possibilities—and their limitations—can teach language educators about the role of generative AI in language learning and the continued importance of teachers and human interaction.

In this episode:

• The Chinese Room, by John Searle https://drive.google.com/file/d/1Mx8BUCwB7u71Gu5ZxgndhH2F5OVEryYc/view?usp=sharing
• Swain, M. (2006). Languaging, agency and collaboration in advanced second language proficiency. In H. Byrnes (Ed.), Advanced language learning: The contribution of Halliday and Vygotsky (pp. 95–108). Continuum.

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 Linguistics at UC Santa Barbara and recipient of the 2026 CALICO Lifetime Achievement Award, reflects on what decades of experience with language-learning technology can teach us about the current AI moment, including how to move beyond the hype to identify what AI can and cannot do and whether AI can reproduce the social, pragmatic, and intercultural dimensions of human interaction. Bin Zou, Professor of Applied Linguistics at Xi'an Jiaotong-Liverpool University in China, examines the opportunities and challenges of generative AI for language teachers, including AI feedback and ways educators can incorporate their own pedagogical expertise into general-purpose AI tools. Visiting Professor of Applied Linguistics at Ohio University Francesca Marino considers AI and language teacher education, including how U.S. universities are preparing future language teachers and CALL researchers to integrate emerging technologies. Finally, Ben Altschuler, CEO of Speakology AI, explains how conversational AI can support language learning through realistic AI avatars and teacher-designed speaking activities.

In this episode:

• CALICO 2027 in Fairbanks, Alaska: https://calico.org/calico-2027/
• Dorothy Chun: https://education.ucsb.edu/people/emeriti-faculty/dorothy-chun
• Bin Zou: https://scholar.xjtlu.edu.cn/en/persons/BinZou/
• Francesca Marino: https://webcmsqa.ohio.edu/cas/linguistics/fmarino
• Ben Altschuler, CEO of Speakology AI: https://speakology.ai/

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 opportunities and the limitations of AI, sharing practical insights for language educators grounded in their current research.

In this episode:

• CALICO 2026 Conference: https://calico.org/calico-2026/
• Robert Godwin-Jones: https://godwinjones.com/
• Jeffrey Maloney: https://jeffreysmaloney.wordpress.com/
• Mingjun Tang: https://researchers.mq.edu.au/en/persons/mingjun-tang/
• Kimberly Vinall: https://blc.berkeley.edu/people/kimberly-vinall
• Emily Hellmich: https://blc.berkeley.edu/people/emily-hellmich

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 have been learning about and working with GenAI.

In this episode:

• The hosts reference: Principles of GenAI-SIPD by Phil Hubbard and Mat Schulze: https://www.igi-global.com/gateway/article/full-text-html/378304
• Phil Hubbard: https://web.stanford.edu/~efs/phil/
• Mat Schulze: https://PantaRhei.press/mat

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 about GenAI, human communication, and language learning. They also encourage educators to experiment with GenAI tools, reflect on their own interactions with them, and consider both their capabilities and limitations.

In this episode:

• Alan Turing (1936), On Computable Numbers, with an Application to the Entscheidungsproblem. Available at https://www.cs.virginia.edu/~robins/Turing_Paper_1936.pdf
• Alan Turing (1950), Computing Machinery and Intelligence. Available at https://courses.cs.umbc.edu/471/papers/turing.pdf
• The Imitation Game (2014), film directed by Morten Tyldum.
• John Searle's Chinese Room. Available at https://rintintin.colorado.edu/~vancecd/phil201/Searle.pdf