LLM-Augmented and Fair Machine Learning Framework for University Admission Prediction

Universities face surging applications and heightened expectations for fairness, making accurate admission prediction increasingly vital. This work presents a comprehensive framework that fuses machine learning, deep learning, and large language model techniques to combine structured academic and demographic variables with unstructured text signals. Drawing on more than 2,000 student records, the study benchmarks logistic regression, Naive Bayes, random forests, deep neural networks, and a stacked ensemble. Logistic regression offers a strong, interpretable baseline at 89.5% accuracy, while the stacked ensemble achieves the best performance at 91.0%, with Naive Bayes and random forests close behind. To probe text integration, GPT-4-simulated evaluations of personal statements are added as features, yielding modest gains but demonstrating feasibility for authentic essays and recommendation letters. Transparency is ensured through feature-importance visualizations and fairness audits. The audits reveal a 9% gender gap (67% male vs. 76% female) and an 11% gap by parental education, underscoring the need for continued monitoring. The framework is interpretable, fairness-aware, and deployable.