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AI tutoring enhances student learning without crowding out reading effort

Author

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  • Fischer, Mira
  • Rau, Holger A.
  • Rilke, Rainer Michael

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.

Suggested Citation

  • Fischer, Mira & Rau, Holger A. & Rilke, Rainer Michael, 2025. "AI tutoring enhances student learning without crowding out reading effort," Discussion Papers, Research Unit: Market Behavior SP II 2025-203, WZB Berlin Social Science Center.
    • Handle: RePEc:zbw:wzbmbh:335027
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    References listed on IDEAS

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    1. Hamsa Bastani & Osbert Bastani & Alp Sungu & Haosen Ge & Özge Kabakcı & Rei Mariman, 2025. "Generative AI without guardrails can harm learning: Evidence from high school mathematics," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 122(26), pages 2422633122-, July.
    2. De Simone, Martin Elias & Tiberti, Federico Hernan & Barron Rodriguez, Maria Rebeca & Manolio, Federico Alfredo & Mosuro, Wuraola & Dikoru, Eliot Jolomi, 2025. "From Chalkboards to Chatbots : Evaluating the Impact of Generative AI on Learning Outcomes in Nigeria," Policy Research Working Paper Series 11125, The World Bank.
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    Cited by:

    1. Gallegos, Sebastian, 2026. "Guidance Over Adoption: Experimental Evidence on AI-Assisted Learning," IZA Discussion Papers 18513, IZA Network @ LISER.

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    More about this item

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    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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