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Opening AI for Language Learning

Opening AI for Language Learning

Autor: Mathias Schulze Philip Hubbard
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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. 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

Schulze & Hubbard, 2026
  • 017 AI detectors do not work
    Oct 27 2026

    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

    Rozwiń Zwiń
    32 min
  • 016 Good (Slow) AI is excellent
    Oct 13 2026

    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

    Rozwiń Zwiń
    26 min
  • Teaching with AI: 2 times 3 takeaways
    Sep 29 2026

    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

    Rozwiń Zwiń
    30 min
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