Generative AI without guardrails can harm learning: Evidence from high school mathematics
Hamsa Bastani, Osbert Bastani, Alp Sungu, Haosen Ge, Özge Kabakcı, Rei Mariman · Proceedings of the National Academy of Sciences · 25 Jun 2025
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- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
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Abstract
Generative AI is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. A key question is how generative AI affects learning-namely, how humans acquire new skills as they perform tasks. Learning is critical to long-term productivity, especially since generative AI is fallible and users must check its outputs. We study this question via a field experiment where we provide nearly a thousand high school math students with access to generative AI tutors. To understand the differential impact of tool design on learning, we deploy two generative AI tutors: one that mimics a standard ChatGPT interface ("GPT Base") and one with prompts designed to safeguard learning ("GPT Tutor"). Consistent with prior work, our results show that having GPT-4 access while solving problems significantly improves performance (48% improvement in grades for GPT Base and 127% for GPT Tutor). However, we additionally find that when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction in grades for GPT Base)-i.e., unfettered access to GPT-4 can harm educational outcomes. These negative learning effects are largely mitigated by the safeguards in GPT Tutor. Without guardrails, students attempt to use GPT-4 as a "crutch" during practice problem sessions, and subsequently perform worse on their own. Thus, decision-makers must be cautious about design choices underlying generative AI deployments to preserve skill learning and long-term productivity.
About this publication
Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.
Placed by: Leading field journal. How sources are ranked.
- Published
- 2025 (16 months ago)
- Citations
- 313 (OpenAlex)
Citations in context
313 citations — 97th percentile among the 33 indexed papers from 2025 in this collection. Citation counts favor older papers; this compares only same-year papers here, not the whole literature.
□ added to this collection in 2026.
author affiliation matches seed institution university of pennsylvania — part of why this paper is in the collection.
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