## 6 Conclusions  
This study demonstrates the viability of machine learning approaches in predicting academic performance using publicly accessible student data. Among the evaluated classifiers, logistic regression emerged as the most accurate and interpretable, attaining an accuracy of 96.2% and an AUC of 0.987. SHAP analysis further revealed that critical predictors such as past failures, absences, and parental education significantly influenced prediction outcomes. The successful application of adversarial debiasing reduced demographic disparities, lowering the demographic parity metric from 0.048 to 0.021 while maintaining high predictive accuracy. Our research contributes to the literature by simultaneously addressing predictive performance, algorithmic fairness, and model transparency within a unified experimental framework. The use of open-access data enhances reproducibility and encourages community-driven improvements in educational data mining. These findings carry strong implications for real-world implementation. Schools and policy-makers can harness such models to proactively identify at-risk students and allocate educational resources more equitably. By integrating explainable and fairness-aware AI, stakeholders can build more ethical and effective decision-support systems. Looking ahead, expanding this framework to multi-class classification, longitudinal modeling, and diverse educational contexts will further validate its robustness. As educational data becomes increasingly digitalized, responsible AI frameworks such as the one demonstrated here can play a pivotal role in enhancing student outcomes while upholding ethical standards. While previous studies have applied fairness-aware models or explainable AI methods individually to educational datasets, our work presents a unified pipeline that combines these techniques to provide both predictive insights and fairness diagnostics. Crucially, we show that threshold tuning after class rebalancing significantly improves model reliability--highlighting the importance of calibration in fairness-sensitive contexts. The interpretability analysis using SHAP complements the fairness evaluation by revealing how individual features contribute to observed disparities. K.K., methodology; K.K., S.K. ([PERSON]), and B.S., software; K.K., S.K. ([PERSON]), S.K. ([PERSON]), and B.S., validation; K.K., S.K. ([PERSON]), and B.S., investigation; K.K., S.K. ([PERSON]), S.K. ([PERSON]), and B.S., writing--original draft preparation; K.K., S.K. ([PERSON]), S.K. ([PERSON]), and B.S., writing--review and editing; K.K., S.K. ([PERSON]), S.K. ([PERSON]), and B.S., visualization. All authors have read and agreed to the published version of the manuscript. This study is partially financed by the European Union-NextGenerationEU, through the National Recovery and Resilience Plan of the Republic of Bulgaria, project No BG-RRP-2.013-0001. Not applicable. The dataset used in this study is available at [[https://archive.ics.uci.edu/dataset/320/student+performance](https://archive.ics.uci.edu/dataset/320/student+performance)]([https://archive.ics.uci.edu/dataset/320/student+performance](https://archive.ics.uci.edu/dataset/320/student+performance)) (accessed on 1 July 2025). The authors declare no conflicts of interest. * [PERSON] and [PERSON] (2017) [PERSON]; [PERSON] A unified approach to interpreting model predictions. In _Advances in Neural Information Processing Systems 30 (NeurIPS 2017)_; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON]; Curran Associates, Inc.: Red Hook, NY, USA, 2017; pp. 4765-4774. * [PERSON] (2019) [PERSON] XAI--Explainable Artificial Intelligence. Defense Advanced Research Projects Agency (DARPA). 2019. Available online: [[https://www.darpa.mil/program/explainable-artificial-intelligence](https://www.darpa.mil/program/explainable-artificial-intelligence)]([https://www.darpa.mil/program/explainable-artificial-intelligence](https://www.darpa.mil/program/explainable-artificial-intelligence)) (accessed on 10 July 2025). * [PERSON] et al.