Event Description
From ethical to environmental to epistemological concerns, AI has received as much backlash as it has accolades. And yet, dividing responses to AI along binaries—good/bad, risk/reward, yes/no—limits our potential responses. How can we think beyond these dichotomies to engage productively with AI in language, literature and culture study? How can we think outside the (black) box?
In this half-day conference, we open a discussion with interlocutors across UC campuses to explore responses to AI within language, literature & culture study today.
Guiding Questions
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What implications do LLMs have for understandings of language? Culture? Literature? Higher Education?
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What approaches and stances are being adopted to address these implications within language/literature/culture teaching and research in higher education across UC campuses? What can we learn from each other?
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What additional support do instructors, graduate students, and undergraduates studying/teaching language, literature and culture need?
Event Details
Friday, February 13, 2026, 10:00 AM - 5:00 PM | B4 Dwinelle Hall and Zoom
Registration: In Person
Registration: Zoom
Description
GenAI provokes existential questions about writing, including its role in the humanities and higher education more broadly. In this panel, we gather together scholars and practitioners who are thinking about writing and AI outside the box–how to teach writing, how to assess writing, how to write itself.
Panelists
Lillian Jones | Assistant Professor of Teaching/Director of Spanish Language Program | UC Irvine
Sophia Minnillo | Postdoctoral Researcher | UC Davis University Writing Program
Ben Spanbock | Continuing Lecturer | UC Berkeley College Writing Program
List of undergraduate research projects to be announced in January 2026.
Check out last years' projects here.
Description
LLMs are profoundly multidisciplinary: at their core, they are built upon the intersection of language and mathematics. And LLMs have been applied, in research, theory and practice, across disciplines. Yet inquiry into AI can easily become siloed. This panel explores the ins and outs of research on AI that crosses disciplines.
Panelists
Moderator: Dorothy Chun | Professor Emeritus of Education | UC Santa Barbara
Annie Lamar | Assistant Professor of Classics, Director of the Low-Resource Language Lab | UC Santa Barbara
Pratik Sachdeva | Senior Data Scientist | UC Berkeley D-Lab
Eric Van Dusen | Outreach and Tech Lead; Lecturer | UC Berkeley College of Computing, Data Science, & Society
Tom van Nuenen | Senior Data Scientist and Lecturer | UC Berkeley D-Lab
Darby Vickers | Assistant Professor of Philosophy | UC San Diego
Thinking Critically About Critical Thinking: Writing, Co-Writing, And Language
Pranav Anand
Professor of Linguistics, Associate Dean of Research for Humanities Division, and Faculty Director of The Humanities Institute, UC Santa Cruz
Abstract: For many humanists, writing is the double of thinking itself. Unsurprisingly, as educators, we have for generations emphasized writing practice as essential to the development of a host of critical thinking skills, including rhetorical structure, close analysis, and comparative reasoning. Writing assignments are thus used not just to evaluate student learning but to directly improve it.
Large language models, with their ability to construct coherent, context-appropriate prose, thus feel like an existential threat. They can generate essays with ease that invalidate our assessment systems. And by encouraging early writers to offload some of the cognitive burden of writing, they rob those writers of the learning that comes from taking on those tasks themselves.
In this talk, I present some reasons not for optimism per se, but realism. Considering the literature of the learning efficacy of writing in psychology, I suggest that however profound the benefits from writing may be in principle, our historic practices have not lived up to that promise. Large language models, I argue, present an important opportunity to isolate the processes recruited during writing, and to make crucial progress on understanding how writing reflects our thinking, and how it shapes it.
In this talk, I present some reasons not for optimism per se, but realism. Considering the literature of the learning efficacy of writing in psychology, I suggest that however profound the benefits from writing may be in principle, our historic practices have not lived up to that promise. Large language models, I argue, present an important opportunity to isolate the processes recruited during writing, and to make crucial progress on understanding how writing reflects our thinking, and how it shapes it.
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