arXiv Machine Learning September 11, 2026 By Xianghui Meng, Yujing Zhang, Jionghao Lin

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

Read the original on arXiv Machine Learning →

PEARL is a task-aware framework that evaluates differentially private synthetic educational data by checking validity, privacy protection, predictive usefulness, and suitability for the intended educational task. In a study of 96 settings, only 12 datasets passed all PEARL checks, with many failures due to missing outcome groups or distorted learning activity order. Even datasets that met privacy and predictive-usefulness criteria sometimes exhibited fairness issues and failed to support knowledge-tracing models, indicating that privacy alone does not guarantee practical usefulness.

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