Generative AI without guardrails can harm learning: Evidence from high school mathematics

Hamsa Bastani, Osbert Bastani, Alp Sungu (), Haosen Ge, Özge Kabakcı and Rei Mariman
Additional contact information
Hamsa Bastani: b Wharton AI & Analytics , Philadelphia , PA 19104
Osbert Bastani: c Department of Operations, Information, and Decisions , School of Engineering and Applied Science , University of Pennsylvania , Philadelphia , PA 19104
Alp Sungu: a Department of Operations, Information, and Decisions , Wharton School , University of Pennsylvania , Philadelphia , PA 19104
Haosen Ge: b Wharton AI & Analytics , Philadelphia , PA 19104
Özge Kabakcı: d Department of Mathematics , Budapest British International School , Budapest 1125 , Hungary
Rei Mariman: e Independent , Philadelphia , PA 19104

Proceedings of the National Academy of Sciences, 2025, vol. 122, issue 26, e2422633122

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.

Keywords: generative AI; education; skill acquisition; personalized tutoring (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:122:y:2025:p:e2422633122

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