As AI becomes more embedded in education, urgent ethical concerns—bias, privacy, transparency—demand scholarly attention. This systematic review examines 34 peer-reviewed studies on AI ethics in education (2020–2024), using the PRISMA framework and a multi-dimensional coding scheme. It identifies stakeholder-specific ethical tensions and highlights gaps in empirical validation, geographic diversity, and policy implementation. Findings cluster into four solution domains: AIED Ethical Frameworks, Ethical Assessment Frameworks, AIED Literacy Frameworks, and Literacy Assessment Frameworks. These are mapped against stakeholder groups and educational levels to expose misalignments and oversights. Visual tools—frequency summaries and a stakeholder–tension Sankey diagram—deepen the analysis. This study offers a structured typology and actionable, context-sensitive recommendations to support equitable and responsible AI integration across K–12 and higher education settings.
Yan et al. (2025) studied this question.