ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION MATHEMATICS: STATE-OF-THE-ART NARRATIVE REVIEW OF TEACHING, LEARNING, AND ASSESSMENT UDC

Abstract

The rapid development of artificial intelligence (AI) systems is transforming educational practices across disciplines, including mathematics education in higher education. AI technologies, particularly generative AI and intelligent tutoring systems, introduce new opportunities for supporting teaching, learning, and assessment while simultaneously raising important pedagogical, ethical, and methodological challenges. This paper presents a state-of-the-art narrative review of recent research on AI in mathematics education in higher education. The review synthesizes literature from the past five to seven years and organizes it around three interconnected domains: teaching mathematics with AI, learning mathematics with AI, and assessment with AI. The analysis shows that AI has the potential to support instructional planning, adaptive feedback, and interactive explanations, thereby expanding possibilities for differentiated learning and formative assessment. At the same time, the literature shows critical concerns related to conceptual understanding, overreliance on automated solutions, assessment validity, authorship, and academic integrity. Particular attention is given to the design of AI-aware tasks and the need to foreground reasoning, interpretation, and mathematical communication in assessment practices. The review identifies key research gaps concerning classroom integration of AI, students’ interaction with AI as a learning partner, and the development of AI-resilient assessment strategies. Implications are discussed for teacher education, curriculum design, and educational policy.