What are the key ethical concerns associated with the use of AI in education, and how do regulations like GDPR and FERPA address these concerns?

Key ethical concerns are data privacy and safety, bias and fairness in AI models, the teacher–student relationship, and transparency and trust. Regulations such as GDPR and FERPA address data privacy by mandating secure, transparent, and accountable handling of student data.

The main ethical concerns identified are data privacy and safety, bias and fairness in AI models, the teacher–student relationship, and transparency and trustfulness. The large volume of student data used by AI systems creates security and confidentiality risks. GDPR and FERPA are regulatory frameworks that require educational institutions to handle data through secure, transparent, and accountable practices. Bias and fairness concerns arise when AI models are trained on non-representative data, so institutions and AI developers must collaborate to evaluate models for fairness and reduce algorithmic bias. The teacher–student relationship can be harmed if educators become overly dependent on AI; technology should enhance, not replace, the human elements of empathy, mentorship, and moral guidance. Finally, transparency is essential: educators and students must understand how AI-generated suggestions are made so that AI tools are used responsibly and trust is established.