AI Tutoring Enhances Student Learning Without Crowding Out Reading Effort

Mira Fischer (), Holger A. Rau () and Rainer Michael Rilke ()
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Mira Fischer: WZB - Social Science Research Center Berlin
Holger A. Rau: University of Göttingen
Rainer Michael Rilke: WHU Vallendar

No 18338, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: We study how AI tutoring affects learning in higher education through a randomized experiment with 334 university students preparing for an incentivized exam. Students either received only textbook material, restricted access to an AI tutor requiring initial independent reading, or unrestricted access throughout the study period. AI tutor access raises test performance by 0.23 standard deviations relative to control. Surprisingly, unrestricted access significantly outperforms restricted access by 0.21 standard deviations, contradicting concerns about premature AI reliance. Behavioral analysis reveals that unrestricted access fosters gradual integration of AI support, while restricted access induces intensive bursts of prompting that disrupt learning flow. Benefits are heterogeneous: AI tutors prove most effective for students with lower baseline knowledge and stronger self-regulation skills, suggesting that seamless AI integration enhances learning when students can strategically combine independent study with targeted support.

Keywords: self-regulated learning; large language models; AI tutors; higher education (search for similar items in EconPapers)
JEL-codes: C91 D83 I21 (search for similar items in EconPapers)
Date: 2025-12
New Economics Papers: this item is included in nep-edu and nep-inv
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