A machine learning based framework for predictive school management using student and faculty analytics

New technologies in education have created a huge amount of data that, when used effectively, can have a major impact on the functioning of an institution and the academic achievement of students. Nevertheless, all existing predictive models are still disconnected and do not integrate historical trends, student-faculty relationships, and trend patterns into a coherent decision-making system. The paper describes an integrated machine learning system that integrates several synergistic AI technologies: (1) deep learning systems (LSTM, GRU, CNN, and Transformers) to model academic growth over time; (2) comprehensible gradient boosting ensembles (XGBoost, LightGBM, and CatBoost) to understandably infer and analyze structured data. (3) graph convolutional networks (GCNs) to encode academic relationships between students, professors, and courses; and (4) data-centric oriented approaches (multitasking, transfer, and federated learning). The framework is tested on two UCI benchmark datasets (n = 649) with fully isolated holdout sets using strict nested cross-validation to prevent data leakage. The framework yields 99.6% and 97.5% predictive accuracy (5.6% and 6.3% improvement over the top baselines) and high recall (99.4% and 96.7%) in classifying at-risk students. Each component has been shown to contribute fully in ablation studies, and the hybrid framework has been shown to outperform state-of-the-art transformed table models (TabTransformer, FT-Transformer, and SAINT) (99.6% vs. 97.2% for the best transformer). Robustness analysis with feature noise and missing data (> 96% accuracy with 20% missing data) demonstrates excellent regression. Fairness assessment indicates that gender and age bias are very small, and mitigation strategies (reweighting, adversarial debiasing) bring the parental education gap down to 0.1%. Cross-domain experiments (mathematics/Portuguese) show a performance loss of -2.3%, indicating internal generalizability, but cross-institutional validation remains to be performed. This framework provides educators with interpretable, actionable insights into evidence-based interventions, demonstrating that for accurate, fair, and robust predictive educational analytics, multi-paradigm AI integration is essential and comprehensive.