DEVELOPING ETHICAL GUIDELINES FOR AI-POWERED ADAPTIVE LEARNING IN EDUCATION

Abstract

The integration of artificial intelligence (AI) in education has transformed adaptive learning by personalizing instruction, automating assessments, and enhancing student engagement. However, the ethical challenges of AI governance, including algorithmic bias, data privacy concerns, transparency gaps, and accountability issues, necessitate a structured regulatory framework. This study develops the Ethical AI Governance Framework for Adaptive Learning (EAGFAL) to address governance gaps and ensure fair, transparent, and responsible AI adoption in education. Using secondary data analysis and comparative case studies, the study examines global AI governance models, regulatory best practices, and ethical risks in AIdriven education. Findings highlight that inconsistent regulatory approaches contribute to disparities in AI deployment, with some regions favoring market-driven AI policies, while others enforce strict legal oversight. The study recommends bias mitigation strategies, explainable AI mechanisms, and robust data governance policies to ensure AI-driven education remains inclusive, ethical, and student-centered. EAGFAL offers practical policy insights for governments, educators, and AI developers, advocating for cross-sector collaboration to align AI innovation with ethical responsibility. Future research should explore long-term impacts, scalability, and evolving governance structures for ethical AI integration in education