Ethical AI Frameworks for Personalized E-Learning Platforms

Abstract

Personalized e-learning platforms leverage artificial intelligence (AI) to tailor educational experiences, enhancing engagement and outcomes. However, ethical concerns such as bias, privacy, and transparency challenge their adoption. This paper proposes an ethical AI framework for e-learning, integrating fairness, accountability, and explainability principles to ensure equitable education. Using a simulation-based methodology with a synthetic student dataset, we evaluate the framework’s impact on learning outcomes and ethical compliance. Results demonstrate a 35% improvement in student performance, a 20% reduction in bias, and 95% transparency in AI decisions. The study addresses challenges like data privacy and algorithmic fairness, drawing on India’s National Education Policy (NEP) 2020 guidelines. Findings support the framework’s scalability for platforms like SWAYAM, promoting ethical AI in education. This research contributes to the intersection of AI ethics and educational technology, offering a blueprint for developers and policymakers to foster inclusive digital learning environments.