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PEARL: A Task-Aware Framework for Evaluating Differentially Private Synthetic Educational Data

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Personalized learning systems rely on real learner data, including performance, behavior, and demographic information, but these data are highly privacy-sensitive. Differentially private (DP) synthetic data can support system development and educational research while reducing exposure of individual learners. Existing evaluations, however, assess privacy and predictive usefulness separately, without determining whether synthetic learner data remain usable for the intended personalized learning task. We introduce PEARL (Privacy-Equivalence Audit and Release Ledger), which approves a DP syntheti

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