Assessment of teacher performance and learner behavior using artificial intelligence in higher education: an integrative review of literature

A systematic integrative review published in Frontiers in Education synthesizes findings from 21 rigorously appraised studies examining the application of artificial intelligence in higher education quality assurance. The analysis explores how adaptive, data-driven methodologies evaluate teacher performance and learner behavior, highlighting key technological trends and avenues to enhance instructional quality and student support systems.

This development signifies an institutional shift from retrospective, manual teaching evaluations toward real-time, automated pedagogical analytics. For higher education governance, adopting AI-enabled quality assurance creates new capabilities for proactive intervention in instructional quality and student outcomes, directly impacting institutional competitiveness and academic accreditation. Managing this shift requires balancing technological potential with methodological rigor and academic trust.

Artificial intelligence is driving a transition toward adaptive, data-driven assessment frameworks in higher education quality assurance.

Forward consideration, not a verified fact.