EXPLORING DATA ETHICS AND RESPONSIBLE AI: A LITERATURE REVIEW IN STUDENT LEARNING CONTEXT

This literature review examines the ethical dimensions of Artificial Intelligence (AI) in student learning environments, focusing specifically on fairness, privacy, and accountability. As AI becomes increasingly integrated into education through intelligent tutoring systems, automated assessments, predictive analytics, and learning support tools, concerns regarding algorithmic bias, transparency, and data protection continue to grow. Drawing exclusively from existing literature, case studies, and documented reports from 2021 to 2025, this review synthesizes current findings on how AI can both enhance and challenge ethical practices in education. The analysis reveals that while AI tools have the potential to promote fairness through personalized learning and standardized evaluation, they also present risks related to cultural bias, privacy intrusion, and unequal access. Case studies consistently highlight issues such as over-reliance on AI systems, lack of transparency in decision-making, and disparities in technological resources across learners and institutions. These concerns underscore the importance of designing AI systems with strong ethical safeguards and implementing institutional policies that prioritize responsible use. The findings suggest that the ethical impact of AI in education depends on careful regulation, transparency, AI literacy, and adherence to robust data governance practices. This review contributes to ongoing academic and policy discussions on how educational stakeholders can ensure that AI tools remain trustworthy, equitable, and supportive of meaningful student learning outcomes.