ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: A GLOBAL REVIEW OF AI-POWERED TEACHING AND LEARNING
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https://doi.org/10.5281/zenodo.20848611##article.subject##:
artificial intelligence, higher education, generative AI, teaching and learning, Technology Acceptance Model, TPACK, adaptive learning, academic integrity, AI literacy, UNESCO, self-regulated learning##article.abstract##
Artificial intelligence, and in particular generative AI systems such as ChatGPT, Gemini, and Claude, has
moved from the periphery to the mainstream of higher education in less than four years. By 2025, multiple international
surveys converge on a single conclusion: AI use among university students is approaching universality, while institutional
governance, faculty competence, and pedagogical theory are scrambling to keep pace. This article synthesises peer-reviewed
empirical research published between 2020 and 2026, together with reports from UNESCO, the OECD, the Russell
Group, the Digital Education Council, the Higher Education Policy Institute, Ellucian, and EDUCAUSE, to present a
global, theoretically grounded account of the current state of AI-powered teaching and learning. The article maps adoption
trends and key technologies, situates them within established and emerging theoretical frameworks including the Technology
Acceptance Model, TPACK and its AI-specific extensions, constructivism, connectivism, and self-regulated learning
theory, reviews benefits and challenges, surveys policy responses across multiple jurisdictions, and outlines future directions
including AI literacy as a graduate attribute, hybrid human–AI pedagogies, and the redefined role of the educator.
The analysis concludes that AI adoption in higher education is no longer a question of trajectory but of governance, and
that the pedagogy of integration, not the technology itself, determines whether AI enhances or undermines learning outcomes.
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