Generative AI Availability, Grades, and Student Satisfaction at a Large University

This large-scale observational study tests the "GenAI substitution hypothesis" — the concern that students offload cognitive effort to Generative AI and earn inflated grades without learning. Using syllabus and administrative data from a large U.S. university (2015–2025; 156,135 students; 87,936 course offerings), the authors measure each course's GenAI susceptibility with a human-validated LLM pipeline that extracts assessment types from syllabi, then apply a difference-in-differences design comparing outcomes before and after ChatGPT's release while modeling COVID-19 effects as persistent or transient. They find no significant differential effect of GenAI availability on grades overall or among previously lower-performing students, and no significant effect on self-reported understanding; effects on subject interest are significant only under a transient-pandemic assumption. The findings temper alarm about grade inflation and satisfaction erosion, complementing AI Availability Student Motivation and the mixed picture in Generative AI Reduced Study Time Math. The null result is notable against theoretical worries about Cognitive Offloading and situates Student Experience concerns in Higher Ed on firmer empirical ground.

Connected Concepts

Connected Articles

Citation

Dumlao, Wang, Xie, Hu, Bar, Chaney, Gold & Teplitskiy (2026). Generative AI Availability, Grades, and Student Satisfaction at a Large University. arXiv:2607.21534. arXiv preprint (cs.CY).