Guidance Over Adoption: Experimental Evidence on AI-Assisted Learning

Sebastian Gallegos

No 18513, IZA Discussion Papers from IZA Network @ LISER

Abstract: This paper estimates the causal effect of a large language model-based study assistant on student behavior and learning outcomes in a natural field setting with real academic stakes. I design and deploy a course-specific AI assistant (GPT-UAI) for undergraduate econometrics and evaluate it through two randomized interventions implemented across seven coordinated course sections at a selective university in Chile. The first intervention targets the extensive margin of use, encouraging GPT-UAI adoption prior to the midterm exam. The encouragement raises the GPT's awareness and reported usage, but does not change its perceived value and does not improve midterm performance. The second intervention targets use at the intensive margin, providing guidance on learning-oriented usage for the final exam. Guidance shifts interactions with GPT-UAI toward tutor-style engagement, increases perceived usefulness by 0.38 standard deviations, improves final-exam performance by 0.21 standard deviations, and raises the probability of earning a passing exam grade by 12 percentage points. The findings suggest that learning gains arise less from adoption than from guiding how students use course-specific AI assistants.

Keywords: generative AI; large language models; higher education; field experiments; randomized controlled trials; student learning; human capital; AI-assisted learning; tutoring; technology in education (search for similar items in EconPapers)
JEL-codes: C93 D83 I23 O33 (search for similar items in EconPapers)
Date: 2026-04
New Economics Papers: this item is included in nep-ain, nep-edu and nep-exp
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