A scoping literature review of artificial intelligence integration in higher education for enhanced teaching, learning, and assessment

Abstract

INTRODUCTION: This review explores the global integration of Artificial Intelligence (AI) in higher education, examining its impact on teaching, learning, and assessment, while addressing implementation challenges, ethical concerns, and opportunities for sustainable, equitable adoption. EVIDENCE ACQUISITION: A scoping review was conducted following PRISMA guidelines. Peer-reviewed studies published between 2020 and 2024 were identified through Web of Science and SciFinder. Eligible articles were thematically synthesized using the Tranfield, Denyer, and Smart (2003) framework to examine AI integration in teaching, learning, assessment, and related challenges. EVIDENCE SYNTHESIS: Seventy-three studies were analyzed, revealing six major themes: AI for teaching support, learning enhancement, assessment, ethical considerations, implementation challenges, and opportunities. AI has enhanced instructional design, real-time feedback, and personalized learning across diverse disciplines. In assessment, AI facilitates automated grading and adaptive testing but raises concerns about integrity and human oversight. Ethical issues - such as data privacy, algorithmic bias, and academic dishonesty - were recurrent, particularly in under-resourced settings. Challenges include infrastructure deficits, misinformation, low AI literacy, and the absence of governance frameworks. However, AI also presents significant opportunities to improve equity, efficiency, and student engagement when integrated responsibly. CONCLUSIONS: AI is reshaping higher education by enhancing pedagogy and assessment, but its adoption must be balanced with ethical safeguards, educator training, and robust policy frameworks. Institutions must prioritize equitable access, digital infrastructure, and human-centered approaches to ensure AI's responsible and effective use in education.

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