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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. 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/oaill/
Unknown Host hosts Opening AI for Language Learning.
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 la
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, Kim
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 lea
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 int
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 re
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
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 cou
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
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
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 impor
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 t
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 quest
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