Equity-Driven AI Frameworks for Faculty Performance Evaluation in Public and Private Universities
Alice AlAkoum, Elvira Nica, Mohammad Abiad
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Source: Crossref
Published: Oct 20, 2026
DOI: 10.1108/978-1-83662-878-120261005
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Abstract This chapter investigates how artificial intelligence (AI) might improve teacher performance evaluation systems by eliminating biases and improving equity in public and private universities. Faculty evaluations are frequently criticized for being subjective, inconsistent, and shaped by institutional cultures. The structural and resource imbalances between public and private institutions exacerbate these issues. This chapter uses AI technology to provide equity-driven frameworks for evaluating faculty contributions in teaching, research, and service. This chapter will delve into the ethical aspects of AI deployment, providing insights into creating systems that reduce biases, increase accountability, and accord with institutional ideals. The study provides theoretical frameworks and practical examples to solve challenges faced by public and private universities and suggests AI-driven solutions for equity.
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