AI in education can improve accessibility and personalization, but it also raises serious governance questions. Systems used for assessment, proctoring, or student profiling may create high-impact outcomes and therefore stricter compliance obligations.
First, categorize your AI tools by impact. A content helper differs from a system that influences grading or progression decisions. High-impact tools require stronger controls around transparency, human review, and evidence of reliability.
Student rights and fairness must be explicit in your design process. Document what the model does, where it can fail, and how staff intervene when outputs look questionable. Avoid over-automation in decisions that materially affect learners.
Proctoring and behavioral analysis deserve particular caution, especially where biometric or emotion-recognition features are involved. These capabilities can cross into restricted territory depending on context and implementation.
For SMEs in edtech, a practical compliance baseline includes: clear user notices, educator oversight workflows, regular performance audits, and incident handling rules. This keeps products usable and trustworthy while meeting regulatory expectations.