Generative artificial intelligence (GenAI) continues to transform education, yet its integration raises ethical concerns such as algorithmic bias, privacy erosion, and diminished autonomy. This integrative review synthesizes literature to clarify how ethical risks emerge and how governance can respond. Using integrative review procedures, the study synthesized theoretical and policy sources from Scopus and Web of Science. Analysis applies three lenses: Critical Data Studies, FATE (fairness, accountability, transparency), and IEEE Ethically Aligned Design, producing a sociotechnical account. Across studies, risks cluster upstream in data and epistemic bias reproducing linguistic, cultural, and socioeconomic hierarchies; midstream in algorithmic failures weakening transparency, accountability, and contestability; and downstream in design and governance deficits eroding consent, recourse, and human-centered implementation. Implications include institutional lifecycle governance and auditing, participatory design, AI literacy, and targeted research priorities, especially intersectional and Global South studies. The review contributes a framework linking structural, operational, and design-level ethics for GenAI in education.
Abu et al. (Wed,) studied this question.