Marifel Grace Capili-Kummer
ABSTRACT
Higher Education Institutions (HEIs) are crucial in upholding educational excellence, as demonstrated by their academic programs, student accomplishments, and graduates’ exceptional performance in government examinations. The evaluation of engineering pre-board examination performance of the School of Information Technology and Engineering is vital for
both students and educators, serving as a critical indicator of preparedness for the licensure examination. In this study, the researcher proposes an innovative application for assessing such performance, leveraging Generative Artificial Intelligence (AI) alongside decision support mechanisms. Traditional evaluation methods often rely on constant metrics and subjective assessments, potentially overlooking nuanced patterns and areas for improvement. The system harnesses the power of Generative AI to analyze examination responses and generate comprehensive insights into individual performance. Through a combination of machine learning algorithms and decision support tools, the approach offers personalized feedback, identifies areas of strength and weakness, and assists educators in tailoring remedial strategies. By integrating cutting-edge AI technology with decision support mechanisms, the system aims to enhance the accuracy and efficiency of pre-board examination performance evaluation in engineering education, ultimately fostering continuous improvement and academic excellence.
Keywords: Decision Tree Algorithm, Decision Support System, Generative AI, Pre-Board Examination Performance
Evaluation
https://doi.org/ 10.57180/clek3292