Analytical Study of Faculty Career Progression Assessment Employing Artificial Intelligence Techniques
Keywords:
Faculty evaluation, career progression, artificial intelligenceAbstract
The evaluation of faculty career progression has traditionally relied on qualitative judgment, institutional policies, and standardized metrics such as teaching effectiveness, research output, and service contributions. However, these conventional approaches often suffer from subjectivity, bias, and limited scalability, thereby restricting their effectiveness in dynamic academic environments. This research paper presents an analytical study of faculty career progression assessment through the integration of artificial intelligence (AI) techniques, aiming to enhance objectivity, predictive accuracy, and decision-making efficiency.
The study synthesizes existing literature on faculty evaluation systems, promotion criteria, and machine learning applications in human resource management. It critically examines traditional evaluation frameworks and identifies key limitations, including gender bias, inconsistency in evaluation standards, and lack of real-time analytics (Files, 2017; Gumpertz et al., 2017). Building upon these insights, the paper proposes an AI-driven framework that incorporates machine learning models such as support vector machines, neural networks, and data mining techniques to analyze multidimensional faculty performance data (Deepak et al., 2016; Huang et al., 2004).
The proposed approach integrates structured and unstructured data, including publication metrics, teaching evaluations, professional development indicators, and behavioral attributes, to generate predictive models for career advancement. Furthermore, the study explores the role of intelligent systems in mitigating bias and improving fairness in evaluation processes through algorithmic transparency and data-driven decision-making (Zhao et al., 2023; Ordoñez-Avila et al., 2023).
Findings indicate that AI-based systems significantly improve the reliability and consistency of faculty assessments, while also enabling early identification of career trajectories and performance gaps. However, challenges related to data quality, ethical considerations, and interpretability of AI models remain critical concerns.
The paper concludes that integrating artificial intelligence into faculty evaluation systems represents a transformative approach, offering enhanced analytical capabilities and equitable decision-making. It highlights the need for further research in explainable AI and institutional adaptability to fully realize the potential of intelligent evaluation systems.
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Copyright (c) 2026 Dr. Andika Prasetyo, Dr. Siti Rahmawati
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