Date
Publisher
EdWorking Papers
We evaluate access to a course-integrated generative AI (GenAI) tutor in a randomized controlled trial involving 2,379 undergraduate students and 30 instructors across multiple disciplines in a large U.S. public university. Among sections of the same course, tutor access reduced final grades by 0.37 standard deviations and learning management system participation by 0.90 standard deviations. Participation declines were similarly large in the full sample, and estimated academic losses were greater among first-generation students. These findings suggest that introducing an AI tutor may displace engagement with existing learning activities without improving academic performance, with potentially differential impact across student groups.
Study at a glance
- Question: How does access to a course-integrated generative AI tutor affect student performance and course engagement?
- Setting and data: Large U.S. public university, 2,379 undergraduate students and 30 instructors across multiple disciplines.
- Method: Randomized controlled trial
Why it matters
Understanding how course-integrated generative AI tutors affect student performance, course engagement, and equity gaps is essential as colleges rapidly adopt automated tutoring tools.
Key findings
- Among sections of the same course, access to the AI tutor reduced final grades by 0.37 standard deviations and learning management system participation by 0.90 standard deviations.
- Participation drops were similarly large across the full student sample, and estimated academic losses were larger among first-generation college students.
- The results suggest that an AI tutor may displace engagement with existing learning activities without improving academic performance, potentially impacting student groups differently.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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