One Click Away: AI Tutoring with Khanmigo in a Two-Year School Experiment
One Click Away: AI Tutoring with Khanmigo in a Two-Year School Experiment
August 2026
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.
Keywords
Artificial intelligence; AI tutoring; Khanmigo; computer-assisted learning; educational technology; field experiment; math achievement; student engagement; response to intervention
Education level
Topics
Tags
Generative artificial intelligence (AI) in education,
Document Object Identifier (DOI)
10.26300/kner-hv33
EdWorkingPaper suggested citation:
Oreopoulos, Philip, and Nina Low. (2026). One Click Away: AI Tutoring with Khanmigo in a Two-Year School Experiment. (EdWorkingPaper: 26-1551). Retrieved from Annenberg Institute at Brown University: <https://doi.org/10.26300/kner-hv33>
Machine-readable bibliographic record: RIS, BibTeX
Related EdWorkingPapers
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.
##### Helping the Many by Tutoring the Few: The Social Multiplier of Early Literacy Instruction
Paul Bingley, Jens Dietrichson, Mette Friis-Hansen, Vibeke Myrup Jensen. August 2026
Elementary schools, Reading and literacy education, Tutoring
Tutoring is almost always evaluated by randomizing students within schools, a design that differences out any spillover onto classmates.
See more from Student Learning
A library of instruments for education research and school improvement
Student Opinion ScaleCategory: Student LearningTags: Motivation, Student engagement
Science Motivation Questionnaire IICategory: Student LearningTags: Motivation, Student engagement
A new model for informed decision-making in education