11:30 - 13:00
Location: 224 - Floor 1
Chair/s:
Akshay Moorthy
Akshay Moorthy - Learning under different information acquisition modes: Experimental evidence
Tanjim Hossain - How do people update beliefs? Evidence from the laboratory
Mira Fischer - AI Tutoring Enhances Student Learning Without Crowding Out Reading Effort
Alessandro Stringhi - Fooling Yourself: how narratives shape beliefs
Stefano Pagliarani - Student creativity and institutions: Evidence from post-communist and EU countries
Submission 48
AI Tutoring Enhances Student Learning Without Crowding out Reading Effort
panel.1-224 - Floor 1-02
Presented by: Mira Fischer
Mira Fischer
BiB - Federal Institute for Population Research
University of Goettingen
WHU - Otto Beisheim School of Management
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 selfregulation skills, suggesting that seamless AI integration enhances learning when students can strategically combine independent study with targeted support.