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@article{184033,
        author = {Dr. Kismat Chhillar and Prof. Kavya Dube and Dr. Sunil Trivedi},
        title = {AI-Driven Multi-Objective Optimization for Equitable Distribution of Teaching Staff: A Framework for Policy, Practice, and Ethical Implementation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {4012-4017},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184033},
        abstract = {This paper introduces a fresh conceptual framework for an AI-powered multi-objective optimization model aimed at tackling the ongoing issue of uneven teacher distribution. By blending insights from education policy, operations research, and AI ethics, it proposes a methodology that goes beyond the usual static allocation methods. The framework utilizes a diverse Teacher Quality Index alongside a thorough Student and School Needs Index to fine-tune teacher assignments, striking a balance between competing goals like educational equity, teacher preferences, and administrative efficiency. It also highlights the crucial aspects of data privacy, the need to reduce algorithmic bias, and the importance of conducting iterative pilot studies. The analysis section delves into the various impacts of this model, covering everything from student outcomes to teacher retention and financial sustainability. Lastly, it offers a forward-thinking view on emerging AI trends and provides actionable recommendations for policymakers and district leaders to promote a future of genuinely evidence-based educational resource allocation.},
        keywords = {Educational Equity, Multi-Objective Optimization (MOO), Algorithmic Bias, Federated Learning, Teacher Quality Index.},
        month = {August},
        }