Access is granted automatically when you accept the terms. We keep a record of everyone who has accepted them and will use the email on your Hugging Face account to tell you about corrections or withdrawn releases.

Million Tutor Moves (MTM) Data Use Terms

The Million Tutor Moves (MTM) data are published by the National Tutoring Observatory as a series of data releases under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. Access is granted on the condition that you agree to the following terms. By requesting access you represent that you have read these terms, that you have authority to accept them on behalf of yourself and any organization you name, and that you will ensure they are observed by everyone to whom you provide the data. One acceptance covers every current and future MTM release published under these terms.

Important: these data have been de-identified, and de-identification is never perfect

Each release describes on its own page how it was de-identified. Some releases replace names, places, schools, dates and similar identifiers with labeled placeholders such as <NAME_1>; others replace them with realistic but fabricated substitutes. Two things are true in every case. First, no identifier in these data should be treated as a fact about a real person, place or institution. Second, because detection is imperfect, a small number of genuine identifiers may survive. The terms below govern both cases.

1. Attribution. You will cite the dataset in any publication, product documentation, or model card that draws on it, using the citation provided on each release's page.

2. No re-identification. You will not attempt to identify any student, teacher, tutor, classroom, school, or district represented in the data — directly, by inference, by linking these data with any other dataset or external information source, or by recovering any information that was removed or replaced — and you will not attempt to contact any individual represented in the data.

3. No recovering what was removed or replaced. You will not attempt, by any method, to determine what a placeholder or substitute stands for, or which identifiers in the data are genuine. This includes cross-referencing identifiers against external sources, rosters, public records, or web search; training or applying classifiers to separate genuine from replaced identifiers; and exploiting statistical or formatting artifacts of the de-identification process. These attempts are prohibited whether or not any individual is ultimately identified.

4. No harm to students, tutors or teachers. You will not use the data in any way that may harm students, tutors or teachers. This includes, but is not limited to: building models or systems that discriminate against students, tutors or teachers on the basis of race, ethnicity, gender, disability, language background, or any other demographic characteristic; and building systems that surveil, rate, discipline, or otherwise penalize tutors or teachers on the basis of their instructional practice.

5. No consequential decisions about individuals. You will not use the data, or any model or tool derived from it, to make or inform decisions about identifiable individuals, including decisions about employment, performance evaluation, discipline, admissions, placement, promotion, or eligibility for services.

6. Permitted uses, including commercial. Research, teaching, evaluation and benchmarking, product development, and the training or fine-tuning of models are all permitted, including by for-profit organizations, provided every other term here is met.

7. Protection against memorization and disclosure. If you train or fine-tune a model on these data and make that model available to others, you will take reasonable, documented measures to prevent it from reproducing verbatim passages of the transcripts, and you will test for this before release. De-identification does not remove this obligation, since verbatim reproduction may still expose residual genuine identifiers.

8. Reporting suspected genuine identifiers. If you come to believe that a particular identifier in the data is genuine — for example because you recognize an individual, or because you encounter it incidentally in the course of ordinary use — you will notify us promptly at sandpiper_admin@cornell.edu, will not disclose it to anyone else or publish it, and will delete or redact the affected material on request. This obligation applies to incidental discovery only; it does not authorize the searching described in Section 3.

9. Secure handling. You will store the data securely and will ensure that any collaborator, employee, contractor, or student to whom you provide access is bound by these same terms.

10. Publication of excerpts. If you quote transcript excerpts in publications, documentation, or demonstrations, you will reproduce the de-identified text exactly as it appears in the data, including any placeholders or substitutes, and will not substitute, verify, or comment on the authenticity of any identifier.

11. Redistribution. CC BY 4.0 permits redistribution. We ask that you link to the release page rather than re-hosting the files, so that others receive the current version, these terms, and any subsequent corrections. If you do share a copy, we ask that you include the release name and version, a link to its page, and any correction notice we have published for it.

12. Sensitive content. These are authentic instructional interactions and may contain content that is sensitive or distressing. Consider this in the design of any human-facing study or product built on them.

13. Your own compliance obligations. You are responsible for compliance with applicable law, your institution's policies, and any human-subjects review requirements that apply to your use.

14. No warranty. The data are provided "as is," without warranty of any kind, express or implied. No representation is made that de-identification is complete.

15. Termination. We may revoke access at any time for violation of these terms, and may require that you delete your copies of the data. Revocation does not relieve you of obligations under Sections 2, 3, 4, 5, 7, and 8. Revocation applies to every MTM release.

16. Corrections and withdrawn releases. If we withdraw a release, or part of one, or publish a corrected version, we will notify everyone who has accepted these terms at the email address on their Hugging Face account. On notice you will stop using the withdrawn material, delete every copy you hold, including derived files and backups, within 10 days, and confirm the deletion in writing if we ask. You may continue to use corrected versions.

17. Changes to these terms. We may revise these terms for future releases. The version you accepted continues to govern the releases you accessed under it. If a revision changes your obligations, we will ask you to accept it again before you access further releases.

18. Combining data and sharing results. You may combine MTM releases with one another. Sections 2 to 5 continue to apply to any combination of these data with other data, and to any query results, aggregates, models, or derived datasets you share.

Dataset Card for MathEd-PII

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.

{
  "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 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 the CC BY 4.0 License (Creative Commons Attribution 4.0 International).

Citation Information

@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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NationalTutoringObservatory/MathEd-PII

Paper for NationalTutoringObservatory/MathEd-PII

Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset

Paper • 2602.16571 • Published Feb 18 • 1