Artificial Intelligence (AI) has increasingly transformed educational systems through adaptive learning technologies that personalize instruction based on learners’ performance, cognitive abilities, and behavioral patterns. AI-powered tools such as intelligent tutoring systems, automated assessment, and predictive analytics have demonstrated potential in enhancing learning efficiency, inclusivity, and student engagement. Despite these advantages, the growing integration of AI into educational environments raises significant ethical concerns related to algorithmic bias, data privacy, transparency, accountability, and learner autonomy. The absence of harmonized ethical and regulatory frameworks across regions has led to fragmented governance approaches, resulting in inconsistencies in how AI-driven educational systems are designed, deployed, and monitored.This study proposes an Ethical AI Governance Framework for Adaptive Learning (EAGFAL) to address existing governance gaps in AI-powered education. Drawing on secondary data and comparative analysis of international AI governance models, the framework integrates ethical principles, regulatory guidelines, and transparency mechanisms to promote responsible AI adoption in adaptive learning systems. EAGFAL emphasizes bias mitigation, data protection, algorithmic accountability, and stakeholder oversight to prevent discriminatory outcomes and safeguard learner rights.Through a qualitative methodology involving policy analysis, case study review, and thematic synthesis of existing literature, this study critically examines the ethical, regulatory, and technological challenges associated with AI-driven adaptive learning. The findings highlight the necessity of governance structures that balance innovation with ethical responsibility, ensuring equitable access to education while maintaining trust in AI systems. The proposed framework offers practical guidance for policymakers, educators, developers, and regulatory institutions seeking to establish ethical standards that support fair, transparent, and accountable AI implementation in education
Oladele et al. (2025) studied this question.