SAFE-T framework proposes that opaque AI decisions and algorithmic bias in education erode stakeholder trust, calling for fairness audits, explainable AI, and participatory governance

The study proposes the SAFE-T Framework, integrating fairness, ethics, and transparency into a structured governance model for AI in education, spanning algorithmic fairness, a transparency index, accountability, and data privacy. Compared with prior literature that discusses AI ethics principles in a dispersed way, the framework consolidates fairness-aware algorithms, a transparency index, GDPR-style compliance, and stakeholder engagement into a single governance model. This is a conceptual model-building contribution based on secondary analysis and thematic analysis of AI education policies, government regulations, and scholarly literature; the paper presents Figure 1 as a framework illustration and reports no empirical test or quantitative evaluation.

The study identifies opacity in AI educational decision-making and algorithmic bias as major barriers to trust, with biases in grading, admissions, and scholarship allocation disproportionately disadvantaging marginalized groups. By synthesizing multiple cases, such as AI grade prediction favoring students from certain racial and economic backgrounds and scholarship allocation disadvantaging rural and lower-income students, it frames dispersed bias evidence as a systemic governance problem. Evidence comes from secondary synthesis of existing literature and cases, such as Mangal and Pardos (2024) on grade prediction and Bogina et al. (2021) on scholarship allocation; the paper itself conducts no new data collection or experiment.

The study argues that accountability requires a hybrid model combining regulatory mandates with participatory governance, including fairness audits, transparency reports, human-in-the-loop decision-making, and AI literacy programs. Relative to positions that emphasize either regulation alone or participatory policy alone, the framework reconciles both into an enforceable and context-sensitive governance path. Based on comparative analysis of policy literature and cases, the paper cites practices such as GDPR-style data protection, transparency indices, and AI literacy programs, but provides no quantitative measurement of implementation effects.

The study addresses educational institutions, policymakers, and AI developers, and applies to settings where governance frameworks are needed for AI assessment, admissions, scholarship allocation, and personalized learning. Its conclusions rest on secondary literature, policy documents, and case synthesis, so they are better suited as a reference for policy discussion and framework design than as empirical conclusions about the effects of any specific AI system. The mechanisms it emphasizes, such as fairness audits, transparency indices, human-in-the-loop review, and AI literacy programs, can serve as starting points for institutional self-assessment and policy drafting.

As a qualitative study based on secondary data, the paper reports no primary interviews, experiments, or quantitative indicators, so the actual effects of mechanisms such as transparency indices and fairness audits remain open questions. Many cases are second-hand citations from existing literature, and their original contexts and implementation details require checking the cited studies. In addition, Figure 1 is a framework illustration, and the main text does not elaborate quantitative criteria for its components; readers seeking operational indicators would still need the cited transparency index and fairness audit literature.