---
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}
}
```