Guidance Over Adoption: Experimental Evidence on AI-Assisted Learning in HigherEducation
Open accessAbstract
This paper studies whether institutions can shape how students use widely available AI toolsand whether this affects academic outcomes. I deploy and evaluate a course-specific AI assistant with two randomized controlled trials across seven course sections at a selective university in Chile. The design combines experimental variation with administrative and survey data in a natural field setting with real academic stakes. An encouragement intervention increases awareness and reported usage but does not improve midterm performance. A guidance intervention shifts interactions toward tutor-style engagement, increases perceived usefulness by 0.38 standard deviations, and improves final-exam performance by 0.21 standard deviations. The results suggest that performance gains may depend less on adoption itself and more on how institutions shape the use of AI tools.
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Gallegos (2026) found that steering learners toward augmentation raises subsequent test scores by 0.22 SD.
“Steering learners toward augmentation raises subsequent test scores in four of the six experiments: by 0.18 SD in Bastani et al. (2025), 0.22 SD in Gallegos (2026), 0.78 SD in Hou et al. (2026), and 0.13 to 0.14 SD in Kreijkes et al. (2026).”
From Experimental Evidence on the Learning Impact of Generative AI (2026) · cites this paper as Gallegos, 2026
Steering learners toward augmentation raises subsequent test scores in four of the six experiments.
“Steering learners toward augmentation raises subsequent test scores in four of the six experiments: by 0.18 SD in Bastani et al. (2025), 0.22 SD in Gallegos (2026), 0.78 SD in Hou et al. (2026), and 0.13 to 0.14 SD in Kreijkes et al. (2026).”
From Experimental Evidence on the Learning Impact of Generative AI (2026) · cites this paper as Gallegos, 2026
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Experimental Evidence on the Learning Impact of Generative AI
Contractor, Z., & Reyes, G. (2026) · arXiv · doi:10.48550/arxiv.2607.08849
- Authors
- Gallegos, S.
- ORCID
- Gallegos, Sebastian
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2026
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W7151289343
- Cited by
- 0
- References
- 0
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- AI-assisted learning, artificial intelligence adoption, student engagement, perceived usefulness, academic performance, higher education, institutional guidance, exam performance
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