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@article{204325,
        author = {Dr. Anagha  Koustubh Joshi and Ms. Geetanjali M. Vaidya},
        title = {From Black Box to Classroom: Ethical and Explainable AI for Educational Decision Support},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2164-2170},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204325},
        abstract = {Artificial intelligence (AI) is increasingly embedded in educational decision-making, from predicting student dropout and personalizing learning pathways to automating assessments and institutional planning. While these applications offer significant promise, many AI systems operate as opaque "black boxes," raising serious concerns around fairness, accountability, and trust. This paper presents a comprehensive review of Ethical and Explainable AI-Based Decision Support Systems (XAI-DSS) in education, synthesizing technical advances in explainability (LIME, SHAP, counterfactual reasoning) with a practical governance framework aligned to global regulatory standards including GDPR, the EU AI Act, and FERPA. We further evaluate bias mitigation strategies and privacy-preserving architectures suited to sensitive student data. Our review demonstrates that explainability and ethical compliance are not competing goals but mutually reinforcing requirements for building AI systems that educators, students, and institutions can genuinely trust. We conclude with a phased implementation roadmap and recommendations for responsible AI adoption in educational environments.},
        keywords = {Explainable AI, Decision Support Systems, Educational Data Mining, Algorithmic Fairness, Learning Analytics, Ethical AI, GDPR, Privacy.},
        month = {June},
        }