Cultural AI Lab

Duke University

The Cultural AI Lab studies what happens when machines become participants in culture — making it, consuming it, judging it, and increasingly judging us.

Undergraduate and graduate students in the humanities, computer science, and data science work together to study the impact of generative AI on cultural production and consumption, and how we might better redesign these systems for humans. We combine large-scale computational analysis with experiments and surveys, and we read closely.

We are currently working on three topics: AI slop, improving AI systems for human creativity, and the downstream effects of LLMs on digital platforms. Current studies under each are on the projects page.

The lab has been generously funded by Schmidt Sciences, through the Humanities and AI Virtual Institute, and by the Social Sciences and Humanities Research Council of Canada.

Selected work

1. Critical Confabulation
   International Conference on Learning Representations (ICLR), 2026. Peiqi Sui, Eamon Duede, Richard Jean So, Hoyt Long.
2. The Social AI Author: Modeling Creativity and Distinction in Simulated Cultural Fields
   AI & Society, 2026. Read paper Edwin Roland, Richard Jean So, Hoyt Long.
3. Why Slop Matters
   ACM AI Letters, 2026. Read paper Cody Kommers and colleagues, with Richard Jean So and Hoyt Long.
4. What Does AI Do for Cultural Interpretation? A Randomized Experiment on Close Reading with Exposure to AI
   ACM Conference on Human Factors in Computing Systems (CHI), 2026. Read paper Jiayin Zhi, Hoyt Long, Richard Jean So, Mina Lee.
5. Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling
   Conference on Language Modeling (COLM), 2026. Read preprint Peiqi Sui, Yutong Zhu, Tianyi Cheng, Peter West, Richard Jean So, Hoyt Long, Ari Holtzman.

Get involved

Faculty, graduate students, and undergraduates interested in this work are welcome to get in touch. Prospective PhD applicants in English should write directly.