Stakeholder Trust and AI in Education: A Policy Perspective on Data Privacy, Bias, and Decision-Making
Abstract
Artificial Intelligence (AI) is increasingly integrated into educational systems, shaping student assessments, admissions, and personalized learning. However, ethical concerns related to trust, fairness, transparency, accountability, and data privacy remain significant barriers to its responsible deployment. This study explores these issues through the SAFE-T Framework (Stakeholder-Aligned Fairness, Ethics, Transparency in AI-Education), a model designed to enhance ethical AI governance in education. Employing a qualitative research design based on secondary data analysis, this study examines AI policies, governmental regulations, and scholarly literature to assess AI governance effectiveness. Findings highlight persistent challenges in transparency, particularly in AI-driven decision-making processes, as well as algorithmic biases that reinforce educational inequities. The study underscores the need for fairness-aware AI models, participatory AI policy frameworks, and accountability mechanisms such as fairness audits and regulatory oversight. The implications of this research emphasize the necessity for educational institutions to integrate explainable AI, ethical oversight, and AI literacy programs to build trust among stakeholders. Proposing structured governance mechanisms, this study contributes to the discourse on responsible AI adoption in education and offers recommendations for ensuring equitable and transparent AI-driven learning environments.
Keywords
AI governance
transparency
fairness
algorithmic bias
ethical AI
References
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