Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification
Educational dialogue is a valuable but sensitive resource. Existing approaches force a tradeoff between governance and accuracy. This paper proposes a fully local cascade framework that reframes de-identification from open-ended entity recognition to constrained privacy triage. A recall-first union proposer over-generates candidate spans, and a context-aware reviewer makes binary Redact/Keep decisions. On math tutoring transcripts, the strongest local configuration achieves 0.958 macro F1, outperforming LLM-only baselines and a commercial API, while running entirely on a single laptop.
[2606.18372] Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification
[Submitted on 16 Jun 2026]
Title:Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification
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Abstract:Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture personally identifiable information (PII) entangled with curricular content, where "Riemann" may refer to a real student or to a mathematical concept. Existing approaches force a tradeoff between governance and accuracy. Commercial Large Language Models (LLMs) can handle this ambiguity but require sending student data to third parties, while local named entity recognition (NER) systems preserve governance but over-redact curricular terms. We propose a fully local cascade framework that reframes de-identification from open-ended entity recognition to constrained privacy triage. A recall-first union proposer combines two lightweight encoders with deterministic rules to over-generate candidate spans; a context-aware reviewer then makes a binary Redact/Keep decision for each candidate using surrounding dialogue and speaker role. We evaluate three reviewer configurations against same-family LLM-only baselines and a commercial API on math tutoring transcripts from two large platforms. The strongest local configuration reaches 0.958 macro F1, compared with 0.767 for a same-family LLM-only baseline and 0.706 for the commercial API, while running entirely on a single laptop. On a targeted challenge set of curricular-personal name ambiguity, the same configuration degrades by only 0.03 F1 versus 0.19 to 0.25 for smaller reviewers. These results suggest that for educational de-identification, problem formulation matters more than model scale.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.18372 [cs.CL]
(or arXiv:2606.18372v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.18372
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Haocheng Zhang [view email] [v1] Tue, 16 Jun 2026 18:18:58 UTC (449 KB)
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