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

2026-07-23

AI Summary

This study investigates whether generative AI leads students to outsource cognitive tasks to artificial intelligence, thereby achieving high grades without genuine learning and potentially undermining their conceptual understanding and interest in coursework. Leveraging syllabi and administrative data from a U.S. university spanning 2015–2025, the authors employ a human-validated large language model pipeline to classify assessment types and quantify course-level susceptibility to AI assistance. Using a difference-in-differences design, they isolate the causal effects of ChatGPT’s release while disentangling transient from persistent impacts of the pandemic. The findings indicate that generative AI does not significantly improve overall or low-performing students’ grades nor meaningfully affect their course comprehension; only under the assumption that pandemic effects are transient is there a modest positive effect on student interest.

Abstract

The spread of generative AI (GenAI) in higher education has raised concerns that students offload cognitive effort to AI, earning high grades without learning. If this "GenAI substitution hypothesis" is true, grades should rise disproportionately in GenAI-susceptible courses--those relying more on assessments like take-home problem sets and essays rather than in-class exams. Substitution could also affect student satisfaction, measured here as self-reported understanding and interest in the subject, which prior research links to assessments. We test the substitution hypothesis using syllabus and administrative data from a large U.S. university (2015-2025; 156,135 students; 87,936 course offerings). We measure courses' GenAI susceptibility using a human-validated LLM pipeline to extract assessment types from syllabi, and use a differences-in-differences design comparing outcomes across courses before and after ChatGPT's release, while modeling COVID-19 pandemic effects as either persistent or transient. We find no significant differential effect of GenAI availability on grades overall or among previously lower-performing students. Effects on self-reported understanding are likewise insignificant; effects on interest are significant only assuming transient pandemic effects. Our findings temper concerns that GenAI inflates grades and reduces students' satisfaction.

Problem

• Generative AI
• student learning
• academic grades
• student satisfaction
• higher education

Innovation

• LLM pipeline
• GenAI susceptibility
• syllabus analysis
• differences-in-differences
• educational assessment