Academic leveling is one of the main challenges in higher education, particularly in STEM disciplines—a challenge that has intensified following academic lockdowns and for which traditional remedial courses have not yielded the expected results.
This study evaluated the impact of an adaptive learning strategy (ALS) designed to reinforce prior knowledge in 1,309 first-year students enrolled in the courses of Computational Thinking, Fundamental Mathematical Modeling, Mathematical Reasoning, and Mathematical Thinking at a private university in Mexico.
Unlike conventional remedial approaches, the ALS offers a flexible, student-centered learning experience through brief adaptive modules integrated into regular courses.
These modules, supported by an artificial intelligence platform, generate personalized learning pathways, adapt content, produce analytics, and facilitate data-driven instructional decision-making.
The research adopted a mixed-methods approach (QUAN > QUAL) and a quasi-experimental design with matched samples to ensure group comparability. Results revealed statistically significant differences in academic performance in favor of the experimental groups compared to the control groups. Additionally, both students and instructors evaluated the ALS positively in terms of usefulness and learning experience.
Findings suggest that an adaptive, integrated, and technology-mediated strategy is a promising alternative to address academic leveling in introductory university courses, offering substantial advantages over traditional remedial methods.
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How to Cite: Aldape-Valdes, P., Rincon-Flores, E. G., Castano, L., & Guerrero, S. (2026). Smart leveling: an AI-driven adaptive learning strategy in higher education. RIED-Revista Iberoamericana de Educación a Distancia, 29(1), 299–320. https://doi.org/10.5944/ried.45482
