Background: Faculty evaluation is essential for improving quality and professional development in higher education. Criticisms of traditional evaluation methods worldwide have encouraged universities to adopt modern, structured, multi-criteria models. This study aims to review faculty evaluation models, criteria, and challenges in leading global universities and suggest practical solutions.
Materials and Methods: This narrative review involved independent searches by two reviewers across ERIC, PubMed, Scopus, Web of Science, and Google Scholar up to April 2025, using relevant keywords such as faculty evaluation, professor appraisal, lecturer assessment, teaching effectiveness, and multi-criteria decision-making. After screening and full-text assessment, studies meeting predefined criteria were selected.
Results: Findings indicate that modern faculty evaluation systems at leading universities successfully combine quantitative and qualitative methods, better recognizing diverse faculty roles. Advanced multi-criteria decision-making models—such as Spherical Fuzzy AHP, Grey MARCOS, and LOPCOW—enable comprehensive evaluations by appropriately weighting teaching, research, student engagement, social responsibility, and ethics. Tools like the balanced scorecard integrated with AHP and TOPSIS support multidimensional assessments aligned with institutional goals. Recent innovations, including shifting from traditional grading to ranking systems and using tailored feedback forms alongside multi-source input, have improved accuracy, transparency, and fairness. However, persistent challenges include student feedback bias, faculty resistance, implementation complexity, and the need for context-specific approaches. To enhance effectiveness, continual process reviews, broader stakeholder involvement, local model adaptation, and integration of diverse data sources are strongly recommended.
Conclusion: Effective faculty evaluation requires integrated, multi-source models, regular updates to criteria, and active faculty engagement. Combining qualitative and quantitative data with attention to local context is essential to promote transparency, fairness, and continuous quality improvement in higher education assessment systems.