AI-Based Performance Management System (PMS) and Employee Performance Appraisal: A Critical Assessment of Annual Performance Evaluations in Tertiary Institutions in Chukwuemeka Odumegwu Ojukwu University, Anambra State

Abstract

The integration of AI-Based Performance management system (PMS) in employee performance appraisal represents a significant advancement in the management of human resources improvement within tertiary institutions as higher education faces increasing demands for transparency, accountability and performance optimization, AI-based PMS offers innovative solutions for assessing academic and administrative staff. This paper examines the role of AI-based PMS in enhancing the effectiveness, fairness, and efficiency of employee evaluations in Chukwuemeka Odumegwu Ojukwu University, with a total population of 2,570, however, a sample size of 651 was extracted from the population using Borg and Gall (1979 formular in determining a finite population. The study adopted correlation survey research design in examining the utilization of AI-Based PMS as it relates to employee performance appraisal. It explores the integration of the role of AI in redefining employees’ performance appraisal and management systems, which is one of the vital consequentiality of Human Resource (HR) in today’s digital era. Additionally, the study discusses the benefits of AI-driven systems, including objectivity, scalability, and real-time feedback, while addressing key challenges such as data privacy, algorithmic bias, and ethical concerns. The paper concludes by highlighting best practices for the responsible implementation of AI-based PMS in performance evaluation, emphasizing the need for a balanced approach that combines technological innovation with human oversight. The findings showed that inaccuracies in evaluation, such as favouritism, lack of proper feedback, or subjective ratings, tend to demoralize staff, leading to reduced commitment, inefficiency, and in some cases resistance to institutional policies. The following recommendations where made from the findings; integrating qualitative measures and periodic algorithmic audits to preserve holistic appraisal, producing interpretable models that can produce human-readable justifications for recommendations, publishing the appraisal criteria, data sources, weighting scheme, and appeal procedures in applicable, and finally establish a centralized AI data management and governance framework.