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

Introduction Artificial intelligence (AI) is transforming quality assurance in higher education by enabling adaptive, data-driven assessments of teaching performance and student learning. This integrative review synthesizes the purposes, methodologies, and outcomes of AI-enabled assessments, examines emerging technological trends, and identifies opportunities for improving teaching quality and learner support. Methods An integrative review was conducted using the Whittemore and Knafl framework, with study identification and selection reported in accordance with PRISMA guidelines. Of 367 records identified, 21 studies were included. Methodological quality was appraised using the Mixed Methods Appraisal Tool (MMAT). Descriptive and thematic analyses were supplemented by keyword network mapping using VOSviewer version 1.6.20. Results Most studies were multi-authored (71.43%) and published in interdisciplinary education and technology journals (57.14%), with publication activity concentrated in 2024 (28.57%) and 2025 (33.33%). The Western Pacific Region accounted for the largest proportion of studies (38.10%), while no studies originated from the African Region. Teacher performance was the predominant assessment focus ( n = 11; 52.38%), followed by learner behavior ( n = 4; 19.05%) and learner performance ( n = 3; 14.29%). Teacher-focused assessments primarily analyzed learner-generated data using machine learning and natural language processing, whereas some learner-focused assessments employed video-based data and computer vision. Machine learning and natural language processing were the most frequently identified technological approaches ( n = 6), followed by computer vision and multimodal or speech recognition approaches ( n = 3). Most implementations followed a data-input, AI-processing, and output-generation structure, with varying levels of complexity. Thematic analysis identified four domains, while keyword network analysis revealed eight clusters. Discussion The findings highlight a fragmented assessment landscape in which teacher performance and learner behavior are predominantly examined independently. Although learner behavior may provide complementary evidence of teaching effectiveness, reciprocal relationships between teaching and learning remain insufficiently explored. Geographic disparities and methodological heterogeneity further constrain the generalizability and institutional applicability of existing evidence. Conclusion AI offers promising opportunities to strengthen teaching evaluation and learner support in higher education. However, broader geographic representation, methodological rigor, validated assessment frameworks, and equitable, human-supervised implementation are essential to ensure that institutional adoption is guided by robust evidence. Systematic Review Registration https://osf.io/ugt4w .