Artificial Intelligence Based Decision Support System for Education Management in Higher Education
Abstract
Universities face challenges in improving the effectiveness of lecturer performance evaluation to support education and research development. Artificial Intelligence (AI) offers the potential to optimize the evaluation process through automation, big data analysis, and smarter decision-making. This research aims to identify and implement strategies for the implementation of artificial intelligence in lecturer performance evaluation systems in higher education. The main focus is to improve the objectivity, accuracy, and efficiency of the evaluation process, so as to provide more meaningful feedback to lecturers. This research uses qualitative and quantitative approaches. Data were obtained through literature studies, interviews with education and technology experts, and analysis of existing lecturer performance evaluation data. An AI system was developed to process and analyze evaluation data by utilizing machine learning algorithms to provide more accurate evaluation recommendations. The result of this research is that implementing artificial intelligence in the lecturer performance evaluation system succeeded in improving the accuracy of the evaluation. The system is able to provide recommendations for lecturer development based on a thorough analysis of their performance. In addition, the time efficiency of the evaluation process was also significantly improved. The conclusion of this study shows that artificial intelligence implementation strategies can be successfully applied in lecturer performance evaluation systems in higher education. With increased objectivity, accuracy, and efficiency, this system makes a positive contribution to human resource management in the academic environment. It also encourages further implementation of AI technology in lecturer performance evaluation to improve the quality of higher education.
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DOI: http://dx.doi.org/10.31958/jaf.v11i2.12120
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