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README.md

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----
-license: mit
----
+---
+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}
+}
+```