Abstract: Early-warning dashboards in higher education typically stop at tagging students as “at-risk,” offering no concrete guidance for remedial action; this limitation contributes to the loss of thousands of learners each year. Approach. We propose an integrated framework that (i) uses a class-balanced Conditional GAN to augment sparse attrition data, and (ii) couples the resulting XGBoost predictor with a four-mode intervention engine—rule-based, few-shot, fine-tuned LLM, and a novel hybrid strategy—to recommend personalised support. Major findings. Training on GAN-augmented records raises prediction accuracy to 92.79% (a 15.46-point gain over non-augmented baselines), while the hybrid intervention generator attains 94% categorical coverage and the highest specificity score (0.63) albeit at a per-student latency of 61s. Impact. By uniting robust risk prediction with high-quality, actionable interventions, the framework closes the long-standing gap between detection and response, furnishing institutions with a scalable path to materially reduce dropout rates across diverse educational settings.

Hassan Silkhi, Brahim Bakkas and Khalid Housni, “Integrating cGAN-Enhanced Prediction with Hybrid Intervention Recommendations Systems for Student Dropout Prevention” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160697

@article{Silkhi2025,
title = {Integrating cGAN-Enhanced Prediction with Hybrid Intervention Recommendations Systems for Student Dropout Prevention},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160697},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160697},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Hassan Silkhi and Brahim Bakkas and Khalid Housni}
}