Does AI Help or Hurt Learning?

Catalina Franco (), Natalie Irmert () and Siri Isaksson ()
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Catalina Franco: Center for Applied Research (SNF) at NHH – Norwegian School of Economics
Natalie Irmert: Department of Economics, Lund University, Postal: School of Economics and Management, Box 7080, S-220 07 Lund, Sweden
Siri Isaksson: Burgundy School of Business Groupe ESC Dijon Bourgogne - CEREN

No 2026:2, Working Papers from Lund University, Department of Economics

Abstract: AI is transforming how students learn, raising concerns about whether it expands educational opportunities or widens existing gaps. We examine this question in a preregistered lab experiment (N=572) in which students study a novel topic under one of three conditions: browsing only (control), AI-assisted, or AI-guided, and then complete an exam without AI access. We find no overall effect of AI access on learning outcomes. However, this average zero effect masks substantial heterogeneity. High GPA women appear to benefit the most from AI-guided access, while the effects on men and low-GPA students are weaker and in some cases negative. We also find that students with AI access attempt fewer practice questions during the study phase. This suggests that studying with AI crowds out other learning activities, but does not lead to an overall change in exam performance. Finally, exploratory analyses of prompt data provide suggestive evidence on why some students benefit more than others. More delegative AI use (measured by copy-pasting practice questions into the chatbot) is associated with attempting more questions but performing worse on the final exam. High-GPA women rely on this strategy the least and perform the best. Overall, AI appears to crowd out some traditional study effort without reducing learning on average, but because its benefits are concentrated among already advantaged students, it may reinforce existing educational inequalities

Keywords: Generative AI; learning outcomes; study behavior; educational inequality; randomized experiment; human-AI interaction (search for similar items in EconPapers)
JEL-codes: C90 D83 I23 J16 O33 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2026-04-09
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