Regulating Artificial Intelligence in Education: Analyzing Legal and Ethical Frameworks for the Deployment of AI and Machine Learning Models in U.S. Educational Institutions

Abstract

Artificial intelligence is increasingly embedded in U.S. educational institutions for tasks such as dropout prediction and student performance monitoring, yet these systems introduce intertwined legal, ethical, and fairness risks. This study develops and evaluates a regulatory-aligned AI pipeline that integrates fairness auditing, bias mitigation, and privacy preservation within an educational context. Using a privacy-safe synthetic dataset modeling realistic demographic, academic, and behavioral patterns, we benchmark five machine-learning models, Logistic Regression, Random Forest, XGBoost, MLP, and SVM, across baseline, fairness-aware, and privacy-enhanced conditions. Fairness audits conducted with the Fairlearn framework reveal notable disparities across academic-risk and access groups, particularly in selection-rate metrics. A manually implemented reweighing mechanism and adaptive thresholding substantially narrow these gaps with only marginal losses in predictive performance. Differential-privacy simulation through Gaussian noise injection demonstrates that privacy reinforcement entails a measurable but manageable accuracy reduction (~1–2%). A human-in-the-loop policy layer emulates U.S. regulatory requirements under the AI Bill of Rights and FERPA by designating high-risk predictions for human review rather than full automation. Collectively, results show that a governance-first machine-learning workflow can achieve strong predictive validity while satisfying emerging ethical and legal expectations for accountability, fairness, and privacy in educational AI deployment. This framework provides a replicable reference architecture for responsible AI adoption across academic institutions and education-technology providers.