How assigned AI use before class shapes active student engagement in class

Dan J. Wang; Neelam Modi Jain; Vanessa Burbano; Jorge Guzman; Daniel Keum; Soomi Kim; Bruce Kogut; Nataliya Wright

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arXiv · 2610.10463

How assigned AI use before class shapes active student engagement in class

Dan J. Wang·Neelam Modi Jain·Vanessa Burbano·Jorge Guzman·Daniel Keum·Soomi Kim·Bruce Kogut·Nataliya Wright

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Abstract

AI learning tools are rapidly entering classrooms, but evidence about whether they help students learn is mixed and rests mostly on test scores. Comparatively less research addresses whether the use of AI changes students' live learning behaviors in class. Here, we report the results of a preregistered field experiment with 759 MBA students enrolled in ten sections of a course, in which each student was randomly assigned two of ten class sessions to prepare for with a purpose-built voice-based AI discussion partner. After two uses of the AI discussion partner, students made about 31% more voluntary contributions in each later class session. Students who used the AI discussion partner more also reported greater comfort speaking up and greater perceived learning, but not greater focus or motivation. These findings suggest that repeated practice with a voice-based AI partner can meaningfully increase students' engagement in class discussion, enhancing a critical intermediate learning outcome.

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Dan J. Wang, Neelam Modi Jain, Vanessa Burbano, Jorge Guzman, Daniel Keum, Soomi Kim, Bruce Kogut, Nataliya Wright. 2026-10-07. How assigned AI use before class shapes active student engagement in class. https://arxiv.org/abs/2610.10463

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