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language:
- en
license:
- mit
- cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
- token-classification
tags:
- math
- education
- pii
- de-identification
- tutoring
pretty_name: MathEd-PII
---
# Dataset Card for MathEd-PII
## Dataset Description
- **Repository:** NationalTutoringObservatory/MathEd-PII
- **Paper:** https://arxiv.org/pdf/2602.16571
### Dataset Summary
MathEd-PII is a dataset focused on de-identifying Personally Identifiable Information (PII) within mathematics education and tutoring transcripts. This dataset contains surrogate ground truth data generated from question-anchored, on-demand mathematics tutoring sessions, providing a valuable resource for training and evaluating PII detection and redaction models in educational contexts.
### Supported Tasks
- `token-classification`, `named-entity-recognition`: The dataset can be used to train models to identify and classify PII entities within educational dialogues.
- `text-generation`: Can be used for evaluating text sanitization and surrogate generation models.
### Languages
The text in the dataset is primarily in English (`en`).
## Dataset Structure
### Data Instances
Each instance in the dataset represents a tutoring session transcript with labeled PII entities and their corresponding surrogate replacements. See an exerpt in the example below.
```
{"transcript": [
{
"role": "volunteer",
"content": "Hi Chloe! What can I help you with today?",
"session_id": 16592,
"sequence_id": 0,
"annotations": [{
"pii_type": "PERSON",
"surrogate": "Chloe",
"start": 3,
"end": 8}]
},
{
"role": "student",
"content": "hello!",
"session_id": 16592,
"sequence_id": 1,
"annotations": []
}
]}
```
### Data Fields
- `role`: The role of the speaker, "volunteer" or "student".
- `content`: The text content of the message.
- `session_id`: The ID of the tutoring session.
- `sequence_id`: The sequence number of the message within the session.
- `annotations`: A list of PII annotations, each containing:
- `pii_type`: The type of PII. In total, there are 14 types of PII (number of instances in parentheses): PERSON (1,424), URL (187), LOCATION (121), GRADE_LEVEL (107), SCHOOL (73), COURSE_NUMBER (40), NRP (Nationality, Religious or Political group;25), AGE (8), DATE (4), US_DRIVER_LICENSE (2), PHONE_NUMBER (2), and IP_ADDRESS (1).
- `surrogate`: The surrogate replacement for the PII.
- `start`: The starting index of the PII in the content.
- `end`: The ending index of the PII in the content.
## Dataset Creation
### Curation Rationale
This dataset was created to address the lack of specialized, open-access datasets for PII de-identification in educational domains, specifically online tutoring. It enables researchers to build safer, privacy-preserving AI tools for education.
### Source Data
The original source data comes from math tutoring transcripts collected from a U.S.-based online tutoring platform.
### Annotations
The dataset includes LLM-generated annotations for PII deteaction and surrogate replacement based on the pre-redacted tutoring transcripts. Note, over-redaction was observed in the original transcripts. The LLM procedure accounted for this by human-in-the-loop evaluation. Please check the [paper](https://arxiv.org/pdf/2602.16571) for more details.
## Considerations for Using the Data
### For Privacy Preservation
This dataset supports the development of privacy-preserving technologies in education, enabling safer sharing and analysis of tutoring data for research and AI development.
### For Math Tutoring Studies
Due to some over-redaction in the original data, this dataset's ability to fully reflect real-world math tutoring processes may be slightly affected, as some mathematical content was inferred by an LLM post hoc rather than derived directly from the raw transcripts.
## Additional Information
### Licensing Information
The dataset is released under dual licenses:
- **MIT License** (typically for accompanying code/scripts)
- **CC-BY 4.0 License** (Creative Commons Attribution 4.0 International) for the dataset content.
### Citation Information
```bibtex
@article{zhou2026utility,
title={Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset},
author={Zhou, Zhuqian and Vanacore, Kirk and Ahtisham, Bakhtawar and Lee, Jinsook and Pietrzak, Doug and Hedley, Daryl and Dias, Jorge and Shaw, Chris and Sch{\"a}fer, Ruth and Kizilcec, Ren{\'e} F},
journal={arXiv preprint arXiv:2602.16571},
year={2026}
}
```
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