FORMULATING ETHICAL PRINCIPLES FOR THE USE OF AI IN ADAPTIVE LEARNING ENVIRONMENTS

The rapid integration of artificial intelligence (AI) in education, particularly through adaptive learning systems, has redefined instructional delivery, enabling personalized learning experiences and improved assessment processes. While these innovations offer significant pedagogical benefits, they also raise pressing ethical concerns surrounding algorithmic bias, data privacy, transparency, and accountability. In response to these challenges, this study proposes the Ethical AI Governance Framework for Adaptive Learning (EAGFAL)—a structured model designed to guide responsible and equitable AI use in educational contexts. The study adopts a qualitative methodology, utilizing secondary data analysis and comparative case studies to examine existing global AI governance models and regulatory best practices. By evaluating international policies and their effectiveness in addressing ethical risks, the research identifies notable disparities in AI regulation. Some regions emphasize market-led innovation with minimal oversight, while others implement stringent legal frameworks to curb misuse and ensure fairness. These inconsistencies contribute to unequal access to safe and ethical AI-driven learning environments. Key recommendations from the study include the implementation of bias detection and mitigation techniques, the adoption of explainable AI tools to improve transparency, and the development of comprehensive data governance strategies. These elements are integrated into the EAGFAL model, which emphasizes ethical accountability, inclusive design, and cross-sector collaboration among educators, policymakers, and AI developers. The findings underscore the urgency of embedding ethics at the core of AI deployment in education. EAGFAL provides a practical roadmap for stakeholders seeking to balance innovation with responsibility, ensuring that AI-powered adaptive learning supports equitable educational outcomes. The study concludes by calling for further research into the scalability, adaptability, and long-term impact of AI governance frameworks in diverse educational settings