Search and Filter
One Click Away: AI Tutoring with Khanmigo in a Two-Year School Experiment
Generative AI has been promoted as the technology that could transform education by providing every student a personal tutor. We provide some of the first large-scale experimental evidence, from a two-year cluster randomized trial in 18 Tennessee middle schools in which randomly assigned students used Khan Academy with its AI tutor, Khanmigo, configured to coach rather than give answers, during existing daily remedial mathematics sessions. Assignment raises math achievement by 1.3 national percentile ranks per term, or about 0.06 to 0.08 standard deviations over a school year; the implied effect of a full year of active participation reaches 0.14 standard deviations. These gains resemble those from Khan Academy practice without AI assistance. One explanation is that students used the tutor infrequently and, when they did, rarely engaged it in substantive mathematical dialogue: 96 percent of students tried Khanmigo at least once, but the median student messaged it on only a third of the days they practiced, and in only 17 percent of the exercise sessions in which they made a mistake. Messages that students did send were mostly bare answers or clicks on suggested prompts. The binding constraint appears to be engagement: realizing the promise of AI tutoring will require getting students to use it, not just giving them access.
Machine-readable bibliographic record: RIS , BibTeX Related EdWorkingPapers Student Learning The Effects of Course-Integrated AI Tutoring on Student Performance and Engagement: A Randomized University Trial Jing Liu , Tracy Sweet , Marty Haoyuan Chen , Jonathan Engelberg , Megan Masters , Martyn Clark , Axel Persaud , Alia Lancaster , Jeffrey Hollingsworth , Jennifer King Rice . October 2026 Generative artificial intelligence (AI) in education , Higher education , Tutoring 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.
Tutoring is almost always evaluated by randomizing students within schools, a design that differences out any spillover onto classmates.
Related Resources from the EdExchange
A library of instruments for education research and school improvement
Student Opinion Scale
Self-Efficacy for Programming and Physics
Science Motivation Questionnaire II
A new model for informed decision-making in education
Design Principles for Accelerating Student Learning with High-Impact Tutoring (updated June 2024)
Tags: Instructional practices, Student supports, Tutoring
Design Principles for Accelerating Student Learning With High-Impact Tutoring
Tags: Instructional practices, Student supports, Tutoring