AI Tutoring Enhances Student Learning Without Crowding Out Reading Effort

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Authors: Mira Fischer, Holger Andreas Rau, Rainer Michael Rilke
Cited by: 1

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.

Fields of Study & Subjects

Intelligent Tutoring Systems and Adaptive Learning Artificial Intelligence in Healthcare and Education Ethics and Social Impacts of AI

Publication details

Journal / source
SSRN Electronic Journal
Publisher
Elsevier BV
Publication date
1 January 2026
Language
EN
Document type
Preprint