International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Leveraging AI to Assess and Promote Dharma-Based Ethical Competencies in Educational Settings

Author(s) Mrs. Madhu Priya
Country India
Abstract The rapid integration of Artificial Intelligence (AI) into educational systems has generated new opportunities for assessing and cultivating ethical competencies among learners. This study explores how AI can be leveraged to evaluate and promote dharma-based ethical competencies—such as compassion, truthfulness, self-discipline, non-harm, and right thinking—within contemporary educational settings. Using an exclusively secondary-data research design, the study synthesizes insights from existing literature on dharma ethics, moral psychology, AI-based assessment models, and global policy guidelines on ethical AI. A systematic thematic analysis of scholarly articles, policy frameworks, and philosophical texts reveals that AI tools—particularly natural language processing, learning analytics, and affective computing—hold significant potential for analyzing reflective writing, behavioral patterns, and ethical reasoning. Findings suggest that AI can support value-based education by offering personalized ethical feedback, facilitating reflective practices, and enabling continuous monitoring of ethical growth. However, risks related to cultural interpretation, algorithmic bias, and over-reliance on automated moral judgment necessitate careful design and human oversight. The study proposes a conceptual framework that integrates dharma principles with AI-driven assessment to enrich students’ moral development. Implications extend to curriculum designers, policymakers, and educators seeking culturally rooted, ethically aligned AI applications in schools.
Keywords Dharma-Based Ethics, Artificial Intelligence in Education, Ethical Competency Assessment, Value-Based Learning, Secondary Data Analysis.
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-08-08
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E-ISSN 2582-2160
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