| --- |
| 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} |
| } |
| ``` |
|
|
