Generative artificial intelligence and personalized learning in higher education: a systematic literature review (2019-2025)
DOI:
https://doi.org/10.64041/riidg.v5i4.75Keywords:
PRISMA, generative artificial intelligence, personalized learning, higher education, systematic review, AI ethics, adaptive learningAbstract
The emergence of generative artificial intelligence (GenAI) has transformed the way higher education institutions conceive the personalization of learning, understood as the adaptation of content, pace and formative trajectories to the individual characteristics of students. Although the literature on artificial intelligence in education is abundant, gaps remain regarding an updated synthesis that specifically articulates GenAI, and not only predictive AI or classic tutoring systems, with personalization processes at the university level. Objective. This systematic review aimed to analyze the evidence available between 2019 and 2025 on the applications, benefits, ethical challenges and pedagogical frameworks associated with the use of GenAI to personalize learning in higher education. Method. The PRISMA 2020 guidelines were followed (Page et al., 2021). The search was conducted in Scopus, Web of Science, ERIC, ScienceDirect and Dialnet, using Boolean search strings that combined terms related to generative artificial intelligence, personalization and higher education, restricted to the 2019-2025 period and to documents in Spanish and English. After screening by title, abstract and full text, and applying inclusion and exclusion criteria, a final corpus of primary studies was assembled and analyzed through a thematic synthesis matrix. Results. The evidence was organized into four categories: GenAI applications (intelligent tutors, adaptive content generation, automated feedback and dynamic learning pathways), benefits (perceived improvement in performance, greater motivation, teaching efficiency and scalability), challenges (algorithmic bias, privacy, technological dependence, the digital divide and academic integrity) and emerging ethical and pedagogical frameworks oriented toward a human-centered implementation. Conclusions. GenAI offers significant potential to scale the personalization of learning in higher education, but its responsible implementation requires institutional governance frameworks, teacher training in critical digital competencies, and specific safeguards for equity and privacy. Implications for educational practice and future research lines oriented toward longitudinal empirical studies are identified.
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