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Title |
Integrating Generative and Machine Learning Models for Predicting University Admission in an AI-based Education System |
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Author |
Rehab Bahaaddin Ashari |
| Citation |
Vol. 25 No. 7 pp. 1-16 |
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Abstract |
Education industry has been transformed due to the development in technology. Artificially intelligent (AI) involvement within the sub-domains of university admissions and registration processes has, in essence, transformed every dimension of higher education administration. The research will explore the current AI adoption within these fields using four key dimensions: automated application review, predictive analytics, conversational agents, and course recommendation systems. While AI offers increased efficiency, reduced biases, and personalization, it also raises crucial ethical dilemmas that cut across issues of transparency, privacy, and implicit biases in the algorithms. This study reviews recent literature to facilitate the benefits and disadvantages of AI in admissions and registration processes, identify relevant case studies, and describe potential future lines of research. Moreover, it presents the implementation of machine learning models such as support vector machine, decision tree and generative model such as transformer to predict chance of admission in the university based on some features such as GRE score, TOFEL score, etc. Moreover, it recommends universities based on ratings according to the admission chances score of the student. |
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Keywords |
AI; Education System; Explainable AI; LSTM, Transformer, Support Vector Machine, Machine learning. |
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