Ethical and Practical Challenges in AI Integration for Education: Stakeholder Perspectives on Trust, Transparency, and Accountability

Abstract

Artificial intelligence (AI) integration into education presents challenging ethical and practical issues as well as transforming possibilities for individualized learning, operational efficiency, and improved outcomes. This report investigates trust, transparency, algorithmic bias, and responsibility in AI-driven educational systems from the points of view of three hundred teachers, educators, and researchers around India. The results highlight a notable knowledge gap: Just 28.33% of respondents are highly familiar with artificial intelligence in education, while 32.67% are completely unfamiliar. Though 35% of respondents are unsure about data privacy policies, 41.67% of respondents believe AI can greatly improve educational outcomes, so they are cautiously optimistic. Only 27.67% of respondents said they were completely aware of how artificial intelligence systems use data. Thus, algorithmic bias became a major issue for 34% of respondents, along with general calls for transparency. The results highlight the need for human supervision since 35.67% of respondents support artificial intelligence use just under such circumstances. This paper comes to the conclusion that tackling these issues calls for strong ethical rules, professional growth to raise AI literacy, and inclusive models for transparency and responsibility. These steps are essential to guarantee fairness and confidence in AI integration, so opening the path for responsible application in learning environments.