PrivPath: Privacy-Preserving Teaching-Path Guidance via Stage–Subject–Textbook Aligned Large Language Models

Large language models (LLMs) offer new opportunities for lesson planning, remediation, and personalized practice, but deploying them in real educational settings remains challenging for two reasons. First, direct use of learner interaction traces can expose sensitive information about student knowledge states and behavioral patterns. Second, unconstrained generation can produce recommendations that are pedagogically inconsistent with the adopted curriculum, such as skipping prerequisite concepts or drifting outside the prescribed textbook scope. We propose PrivPath, a stage–subject–textbook-aligned framework for privacy-preserving curriculum planning that explicitly separates on-device learner modeling from server-side content generation. Its core module, Tri-Index Private Path Planning (TIPP), first restricts planning to a scoped curriculum graph defined by the target educational context, then privatizes learner mastery summaries under local differential privacy before transmission, and finally computes a curriculum-feasible instructional path that is coupled with evidence-grounded constrained generation. This design preserves personalization while reducing reliance on raw student traces and improving controllability of the generated teaching sequence. Experiments on three educational interaction datasets paired with open-textbook curriculum graphs show that PrivPath substantially improves structural validity and privacy robustness. In particular, it raises graph feasibility from 82.5% to 99.2% relative to TriIndex-RAG, improves the offline pedagogical utility proxy ΔAUC from 0.013 to 0.018, and lowers membership inference AUC from 0.74 to 0.52 at εloc=1.0. These results suggest that curriculum-aligned path planning, privacy-preserving learner adaptation, and practically useful LLM-based educational generation can be achieved within a unified framework.