Opening AI for Language Learning

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. ([email protected])

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

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 below) The whole booklet is UNESCO (2025) AI and the future of education: disruptions, dilemmas and directions.
• Andreas Horn: Rethinking education in the age of Artificial Intelligence (page 38)
• Emily Bender: We do not have to Accept AI (much less GenAI) as inevitable in education (page 41)
• Emily M. Bender: https://faculty.washington.edu/ebender/
• Andreas Horn: https://www.linkedin.com/in/andreashorn1/

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.eduhk.hk/en/experts/professor-stockwell-glenn
• XXIVth International CALL Research Conference in Hong Kong (Dec 2-4, 2026): https://call-research.org/conferences/call2026/

Glenn's papers:

• Professional development and learner training for AI. In Y. Wang, A. Alm, & G. Dizon (Eds.), Insights into AI and language teaching and learning (pp. 203–218). Castledown Publishers. https://archives.castledown.com.au/chapters/ai-insights/9781763711600-12.pdf
• Legal, affective, and policy dimensions of the AI ecosystem in language education. International Journal of Computer-Assisted Language Learning and Teaching, 16(1), 1–14 https://www.igi-global.com/gateway/article/404017

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 conferences that take place during breaks and in the evening.

• General themes
  • Mix of skepticism and excitement
  • Some hype and naive applications, but less than expected
  • Highlighting of ethics and critical AI Literacy (E.g., CALL Interest Section panel on AI ethics: https://submissions.mirasmart.com/TESOL2026/Itinerary/PresentationDetail.aspx?evdid=463 )
  • AI-focused exhibitors, including small-scale startups but also Duolingo
• Two standout presentations (among many!)
  • Miriam Moore (University of North Georgia): four-part critical AI literacy framework. She recommends building a personal ethical checklist and conducting a privacy audit of AI platforms you use. https://submissions.mirasmart.com/TESOL2026/Itinerary/EventsAAG.aspx
  • Jane Freeman (University of Toronto). Using a puppet-puppeteer analogy, she discusses the difference between cognitive offloading (for learners, AI as a shortcut to task completion—human is the puppet) and distributed cognition (AI as a collaborative partner—human is the puppeteer). She also noted (correctly) that “None of us are AI literate.” https://submissions.mirasmart.com/TESOL2026/Itinerary/EventsAAG.aspx
• Exhibitors
  • Flowspeak: Semi-structured scenarios where ESL students interact with an AI to move from B1 to B2 to C1 CEF levels. https://www.flowspeak.io/
  • PangeaChat: Text or audio chat with others with AI support; also has an option for independent interaction with a chatbot. https://pangea.chat/
  • Unicorn Tutor AI: Transforms an institutional curriculum into AI lessons supporting interactive practice. https://unicorntutor.com/
  • ESLVideo’s Chattybots: Activities such as conversations, role plays, picture descriptions conducted with an AI chatbot. https://eslvideo.com/
• TESOL publications on AI
  • Christina Kitson. AI Zip Guide
  • Samaneh Eslamdoost. AI Enhanced ELT: Innovative Strategies to Transform your Classroom
  • William Newman. AI No-Prep Activity Cards

See https://submissions.mirasmart.com/TESOL2026/Itinerary/EventsAAG.aspx for the full convention program.

And: https://www.TESOL.org/