AI-Powered Academic Program Effectiveness Evaluation System with an Integrated Recommendation Engine for Bsit and Bscs Programs

The growing complexity of higher education needs the institutions to embrace sophisticated, data-driven strategies that enable them to constantly assess and advance the academic programs. The proposed paper is the creation of an Academic Program Effectiveness Evaluation System based on AI and aimed at collecting, integrating, and analyzing multidimensional data on the Bachelor of Science in Information Technology (BSIT) and Bachelor of Science in Computer Science (BSCS) programs. The system employs the machine learning algorithms, namely: Random Forest, Linear Regression, and K-Means Clustering, as well as the Natural Language Processing to perform predictive analytics and classify performance trends, and obtain insights based on qualitative stakeholder feedback. The platform produces recommendations through a cohesive system of program evaluation to improve curriculum design, enhance student support, and improve the overall outcomes of the program. The suggested system will facilitate the evidence-based decision making of the administrators, faculty, and stakeholders, which will aid in the continuous quality improvement of the academic institution.

Natural Language Processing (NLP), Artificial Intelligence, Random Forest, Linear Regression, K-means Clustering, Program Evaluation.

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