For the 2026 MLA convention, I will participate in two workshops and two panels. Here are the details:

Workshop: Planning for What’s Next with AI on Your Campus

Drawing on the work of the MLA’s AI task forces, this workshop will explore how generative AI affects scholarship, teaching, academic labor, and marginalized student populations, and the critical stances that have developed in response. After lightning talks, participants will join discussion groups to develop pedagogies, policies, or program recommendations to take home to their institutions.

Presentation: What the FOSS? Lessons from the Electronic Literature Community on Creating Open Access Software

In this presentation I argue that the electronic literature (elit) community offers a vital model for how the humanities can ethically navigate generative AI by embracing free and open-source software (FOSS) principles. Drawing on Montfort and Wardrip-Fruin’s Acid-Free Bits white paper from 2004, I identify elit practices they highlight– such as using open-source platforms, documentation, code validation and porting, using plain-text rather than compiled code, and more– to create and sustain creative work despite waves of technological obsolescence. Using the computational poetry journal Taper as a case study, the talk shows how publishing software-like literary works under libre licenses fosters community, remix culture, and creativity in ways that will outlast work created with proprietary, corporately developed software. Extending these lessons to the AI era, it proposes that adopting open-source methodologies and engaging with accessible models such as OLMo 2 can help humanists shift from passive consumption to transparent, collaborative, and community-driven knowledge production. The FOSS ethos thus equips humanities research not only to survive technological change but to help shape a more equitable and open digital ecosystem.

Workshop: When Do We Need to Know What Is AI and What Is Student Writing? How Can We Know?

During this workshop, attendees learn about approaches to distinguishing student writing from AI and discouraging AI use that interferes with learning. Speakers address fairness and bias, instructor labor, student privacy, and teacher-student relationships and then conduct activities that explore designing for intrinsic motivation, multimodal assignments, ungrading, proctored writing, process tracking, and AI detection.

Panel: Student-Centered AI and DH Practices

I will be presiding this panel, which I convened as part of my work in the MLA Committee on Information Technology.