Active learning methods using generative AI and meaningful learning: systematic review
DOI:
https://doi.org/10.5281/zenodo.23221171Keywords:
AI-based methodologies, meaningful learning, systematic reviewAbstract
The incorporation of generative artificial intelligence into higher education opened up new possibilities for
developing active methodologies, although its contribution to meaningful learning required closer examination. The objective of this research was to conduct a systematic review and bibliometric analysis of the scientific literature on active methodologies enhanced by generative artificial intelligence and their relationship to meaningful learning in the university setting. A conceptual and quantitative analytical literature review was conducted. The search was conducted between February and May 2026 in Scopus, Web of Science, and SciELO, and included publications from 2017 through May 2026. The PRISMA 2020 guidelines were followed to document the identification and selection of studies. The bibliometric analysis examined the temporal evolution of the publications, the first author’s affiliation, and the scientific journals; a review of the articles allowed for the characterization of the pedagogical strategies and learning outcomes addressed. A higher concentration of publications was observed in 2024, as well as a distribution of the studies across institutions in different geographic contexts and journals specializing in education and technology. The studies examined uses of generative artificial intelligence in tutoring, collaborative activities, and feedback processes, but employed different criteria to assess learning. It was concluded that incorporating these elements into active learning methodologies provided opportunities to enrich the college experience; however, participation or performance alone did not demonstrate meaningful learning.
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