Opening AI for Language Learning

Mathias Schulze, Philip Hubbard

Aimed at language educators seeking clarity, practical insights, and critical reflections in the rapidly changing AI landscape, the Opening AI for Language Learning (OAILL) podcast explores the evolving intersection of linguistics, pedagogy, and AI in language teaching and learning. Initiated by the Language and Applied Research Center at San Diego State University (SDSU-LARC), it features hosts Mat Schulze, Professor of German and Director of SDSU-LARC, and Phil Hubbard, Senior Lecturer Emeritus at the Stanford University Language Center, longtime colleagues with decades of experience in technology for language education. In place of the all-too-common hype surrounding AI, they offer conversations in which they and occasional guests share their research and practice-informed perspectives, commentaries on others’ work, and their own professional and personal AI experiences.

We are grateful for the support for Opening AI for Language Learning by the Language and Applied Research Center at San Diego State University and the Southern Area International Languages Network – SAILN – which is part of the California World Languages Project.

Our producer and editor is Chris Brown. Mari Ocando Finol is the production coordinator of OAILL. Our music was composed by Tillmann Spiegl. Live conversations are moderated and the podcast is promoted by Shahnaz Ahmadeian. Evan Rubin is our publicist.

Episodes drop on Tuesday every 2 weeks.

And remember: Artificial intelligence is no substitute for natural ignorance.

Links:

Phil Hubbard: https://web.stanford.edu/~efs/phil/

Mat Schulze: https://pantarhei.press/mat/

The PantaRhei.press blog about OAILL: https://pantarhei.press/oaill/

The SAILN website about OAILL: https://larc.sdsu.edu/sailn/oaill

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#18
Today · 32 min
018 Can chatbots tutor?
Can chatbots really tutor language learners—or are they just good at sounding like they can? In this episode, the hosts explore what it takes for AI to move beyond conversation and become an effective language tutor. Drawing on decades of research in computer-assisted language learning (CALL), they connect lessons from traditional intelligent tutoring systems to today’s AI chatbots. Along the way, they look at tools like Google Translate’s practice mode and consider how chatbots deliver feedback, correct errors, and guide learners through language practice. But fluent conversation doesn’t necessarily mean effective teaching. Join the hosts as they unpack the promise and limitations of AI tutoring and ask what it really takes for chatbots to support language learning. In this episode: Hubbard P, Siskin CB. Another look at tutorial CALL. ReCALL. 2004;16(2):448-461. doi:10.1017/S0958344004001326 Levy, Michael, Computer-Assisted Language Learning: Context and Conceptualization (Oxford, 1997; online edn, Oxford Academic, 31 Oct. 2023), https://doi.org/10.1093/oso/9780198236320.001.0001, accessed 22 Oct. 2026. Heift T, Schulze M. Tutorial computer-assisted language learning. Language Teaching. 2015;48(4):471-490. doi:10.1017/S0261444815000245 Schulze, M. (2025). ICALL and AI: Seven lessons from seventy years. In Y. Wang, A. Alm, & G. Dizon (Eds.), Insights into AI and language teaching and learning (pp. 11–31). Castledown. https://doi.org/10.29140/9781763711600-02
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#17
October 27 · 31 min
017 AI detectors do not work
In this episode, Mat and Phil continue to explore Sam Illingworth’s Slow AI, this time turning their attention to AI detectors. Beyond the question of whether AI detectors work, the hosts discuss the technical, ethical and pedagogical concerns surrounding their use in education. They address the risk of false positives, the ways in which second-language writers may be disproportionately affected, and examples of students altering their writing or using AI-humanizer tools to avoid AI detection. They also consider the potential harms of AI detectors to teaching and learning, including its use of energy and water, the proliferation of AI slop, and the possibility of addictive use and brain rot. In this episode: Sam Illingworth’s Slow AI: Knowing When to Use AI and When to Leave It Alone, https://samillingworth.com/book Sam Illingworth’s substack: https://substack.com/@samillingworth Another of Illingworth’s books (open access): Illingworth, S., & Forsyth, R. (2026). GenAI in higher education: Redefining teaching and learning. Bloomsbury Publishing. https://www.bloomsburycollections.com/monograph?docid=b-9781350535824
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#16
October 13 · 25 min
016 Good (Slow) AI is excellent
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 book Slow AI: Know When to Use AI and When to Leave it Alone, 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
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#15
September 29 · 30 min
Teaching with AI: 2 times 3 takeaways
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
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#14
September 15 · 29 min
What's next, AI? In teacher education
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 will likely 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
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#13
September 1 · 28 min
The Chinese Room Argument
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.
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#12
August 18 · 36 min
CALICO 2026 in Oxford (Part 2)
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://www.ohio.edu/directory/francesca-marino Ben Altschuler, CEO of Speakology AI: https://speakology.ai/
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#11
August 4 · 33 min
CALICO 2026 in Oxford (Part 1)
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
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#10
July 21 · 24 min
What have you GenAI-SIP(pe)D lately?
Mat and Phil reflect on what their own Sustained Integrated Professional Development (SIPD) around GenAI is like 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. They also share what bothers, intimidates, and overwhelms them when 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
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#9
July 7 · 30 min
AI as the Imitation Game
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
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#8
June 23 · 30 min
To AI or Not to AI?
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/
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#7
June 9 · 32 min
AI and learner training: Interview with Glenn Stockwell
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
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#6
May 26 · 36 min
TESOL goes AI
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/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: https://www.tesol.org/professional-development/publications-and-research/tesol-press-bookstore/ 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 And: https://www.TESOL.org/
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#5
May 12 · 32 min
AI and professional development according to Glenn Stockwell
In this episode, Mat and Phil discuss a book chapter by Glenn Stockwell, in which he writes about professional development and learner training for effective use of AI. The chapter highlights the importance of ongoing reflection, and documenting your professional growth with AI. Glenn is the editor in chief of the journal Computer Assisted Language Learning and a professor of Applied Linguistics at the Education University of Hong Kong. In this episode: Glenn Stockwell: https://www.eduhk.hk/en/experts/professor-stockwell-glenn Glenn Stockwell's chapter: Professional development and learner training. https://castledown.online/reference/9781763711600-12/ The book with Glenn’s chapter: Yijen Wang, Antonie Alm, and Gilbert Dizon (Eds.), Insights into AI and language teaching and learning. Castledown Publishers. https://castledown.online/reference/9781763711600/ Mat’s chapter in the same book: ICALL and AI: Seven lessons from seventy years. https://doi.org/10.29140/9781763711600-02
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#4
April 28 · 30 min
Welcome to Sustained Integrated Professional Development
Covering the final part of their paper, Mat and Phil first outline the concept of GenAI Sustained Integrated Professional Development and then discuss how language teachers can build their AI competence through the seven guiding principles of GenAI-SIPD. Understand the basics of GenAI to make informed use decisions. Experience GenAI from the language learner perspective. Testing AI-mediated tasks and materials in the role of your students Practice learning an L2 yourself using GenAI Start small and look for uses of GenAI that have immediate value for your current teaching context. Think of GenAI-SIPD as GenAI “sipped” with consistent (ideally daily) microlearning. Evaluate AI options critically and reflectively. Always consider ethical issues in your and your students’ GenAI use. Seek out and nurture partnerships with peers. In this episode: Principles of GenAI-SIPD in Phil Hubbard and Mat Schulze (2025): https://www.igi-global.com/gateway/article/full-text-html/378304 Darling-Hammond, L., Hyler, M. E., & Gardner, M. (2017). Effective teacher professional development. Learning Policy Institute. DOI: 10.54300/122.311 https://learningpolicyinstitute.org/sites/default/files/product-files/Effective_Teacher_Professional_Development_REPORT.pdf Matherson, L. H., Wilson, E. K., & Wright, V. H. (2014). Need TPACK? Embrace sustained professional development. Delta Kappa Gamma Bulletin, 81(1). Mollick, E. (2024) Co-Intelligence. Living and Working with AI. https://www.penguinrandomhouse.com/books/741805/co-intelligence-by-ethan-mollick/
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#3
April 14 · 44 min
GenAI Stuff you need to know now (part 2)
Mat and Phil return with six additional knowledge areas and skills that language teachers need in order 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 teaching: Motivating Sustained Integrated Professional Development. International Journal for Computer Assisted Language Learning and Teaching https://www.igi-global.com/gateway/article/full-text-html/378304
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#2
March 31 · 36 min
GenAI stuff you need to know now (part 1)
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 review and expand on these areas from their paper: AI and the future of language teaching: Motivating Sustained Integrated Professional Development (SIPD). This is the second episode of the series and also the second discussion of the position paper on SIPD. Episode 1 looked at the technical underpinnings of GenAI. In this episode: Hubbard & Schulze (2025). AI and the future of language teaching: Motivating Sustained Integrated Professional Development. International Journal for Computer Assisted Language Learning and Teaching https://www.igi-global.com/gateway/article/full-text-html/378304 UNESCO. (2024). AI competency framework for teachers. UNESCO Publishing. Available at https://unesdoc.unesco.org/ark:/48223/pf0000391104 Educause Framework for AI literacy in teaching and learning (2024) https://www.educause.edu/content/2024/ai-literacy-in-teaching-and-learning/faculty-altl ISTE Whitepaper about evolving teacher education in an AI world: https://1818747.fs1.hubspotusercontent-na1.net/hubfs/1818747/2024_ISTE_whitepaper_EvolvingTeacher_Ed_in_an_AI_World.pdf Paradox Learning AI toolkit for educators: https://paradoxlearning.com/wp-content/uploads/2023/09/AI-Toolkit-for-Educators_v3.pdf Emily M. Bender & Alex Hanna (2025), The AI Con https://thecon.ai/ Additional reading: Ohashi L. & Hubbard P. (2025). Generative AI ethics: Emerging principles for language teachers. In Ohashi L., Hills M., & Dykes R. (Eds.), Artificial intelligence in our language learning classrooms. Candlin & Mynard. https://www.candlinandmynard.com/uploads/1/2/5/0/12502105/chapter_5_open_access.pdf https://www.candlinandmynard.com/genai1.html
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#1
March 17 · 40 min
What the LLM?
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? In this inaugural episode of Opening AI for Language Learning (OAILL), Mat and Phil open the podcast by answering these questions and then discussing key implications for language teaching and learning. The OAILL podcast was inspired by their collaboration on the 2025 paper "AI and the Future of Language Teaching: Motivating Sustained Integrated Professional Development." International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 15(1), 1-17. https://doi.org/10.4018/IJCALLT.378304 In this episode: Phil Hubbard: https://web.stanford.edu/~efs/phil/ Mat Schulze: https://PantaRhei.press/mat Emily Bender: https://faculty.washington.edu/ebender/ Open-access position paper on Sustained Integrated Professional Development for GenAI (GenAI-SIPD): https://www.igi-global.com/gateway/article/378304
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