Implications of Generative AI in Higher Education: A Systematic Review and Governance Framework

Abstract

This study analyzes the scientific evidence (2016–2026) regarding the integration of Generative Artificial Intelligence (GenAI) into undergraduate education, framed within a model of socio-technical tensions and multilevel governance. This systematic review followed the PRISMA 2020 protocol across five databases (IEEE Xplore, Scopus, WoS, SciELO, and Redalyc) using a Five-dimensional Boolean strategy (technological, cognitive-pedagogical, ethical, academic integrity, and governance). Following the screening of 134 records and the application of CASP criteria (threshold > 7/10), 38 articles were selected. The results reveal a hegemony of Scopus (79%) and an exponential acceleration of publications in 2025 (74%). The ecosystem is dominated by Large Language Models (45%), with primarily instrumental use in academic writing (27%) and learning support (22%). A critical trade-off was identified between operational efficiency and risks of cognitive atrophy, algorithmic bias, and privacy concerns. The research concludes that GenAI integration is a phenomenon of mediated contingency, where educational success depends on systemic alignment. A critical governance gap is identified due to the lack of operationalization of human oversight, compromising the integrity of the ecosystem. In response, a Responsible Integration Framework is proposed, underpinned by multilevel validation mechanisms. Within this framework, national regulation (Macro) is translated by the institution (Meso) so that human oversight in the classroom (Micro) transcends ethical ideals to consolidate as a functional, sovereign, and profoundly ethical pedagogical praxis.