Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset
arXiv:2602.16571v3 Announce Type: replace Abstract: Large-scale sharing of dialogue data is key to advancing the science of teaching and learning, yet rigorous de-identification remains a major barrier. In mathematics tutoring transcripts, numeric expressions frequently resemble structured identifiers (e.g., dates or IDs), leading generic Personally Identifiable Information (PII) detection systems to over-redact core instructional content and reduce data utility. This work asks how to detect PII while preserving educational utility, focusing on this "numeric ambiguity" problem. We introduce Ma
The proliferation of dialogue data for AI training, combined with increasing scrutiny on privacy, necessitates advanced de-identification techniques.
This work addresses a practical barrier to leveraging educational AI datasets, which is crucial for the development of more advanced and ethical AI tutoring systems.
Improved de-identification methods will allow for broader and more effective sharing of sensitive educational data, accelerating research and development in AI for education.
- · AI researchers (NLP)
- · EdTech companies
- · Students (indirectly through better AI tutors)
- · Generic PII detection systems (highlighting their limitations in specialized dom
More accurate and usable math tutoring dialogue datasets will become available for research.
This could lead to faster progress in developing advanced AI tutors capable of nuanced mathematical instruction.
Wider adoption of domain-specific PII handling might set new standards for data privacy and utility across different specialized AI applications.
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