Artificial Intelligence Applied to the Teaching of Exact Sciences
DOI:
https://doi.org/10.70577/cieninter.v4i3.132Keywords:
artificial intelligence; exact sciences; mathematics; physics; chemistry; STEM; generative AI; systematic review.Abstract
The rapid development of artificial intelligence (AI), particularly intelligent tutoring systems, adaptive learning, and generative artificial intelligence (GenAI), has expanded teaching possibilities in mathematics, physics, and chemistry. This article systematically synthesizes recent evidence on the applications, effects, opportunities, risks, and pedagogical conditions of AI in exact-science education. A PRISMA 2020-oriented systematic review was conducted using structured searches of literature published from 2022 to September 2026 in ERIC, PubMed/PMC, and major academic publishing platforms (SpringerLink, ScienceDirect, IOPscience, ACS Publications, and Frontiers). Peer-reviewed studies addressing mathematics, physics, chemistry, or STEM education and reporting learning outcomes, teaching practices, assessment, or pedagogical integration of AI were prioritized. The analytical corpus comprised 20 recent works, including empirical studies, systematic reviews, and meta-analyses. Findings consistently indicate benefits for personalization, immediate feedback, explanation generation, problem-solving support, activity design, and formative assessment. Recent quantitative evidence suggests moderate positive effects on STEM learning, although substantial heterogeneity remains across educational levels, disciplines, and research designs. Major limitations include inaccurate or plausible-but-false responses, weaknesses in complex reasoning and spatial representations, cognitive overreliance, academic integrity, privacy, and unequal access. AI is therefore most educationally valuable when it mediates reasoning rather than replacing students' cognitive activity.
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Copyright (c) 2026 Gina Maribel Riera Calle, Jose Luis Vimos Calle, Diego Geovanny Arias Sinchi
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