CoAuthor dataset

Sharks, Lee

SPXI instrument · method 0.9 of 87, level 0 (Measurement): Capture Registry (longitudinal observation). An instrument of SPXI, the Semantic Packet for eXchange & Indexing; the whole practice is itemized at spxi.dev/methods.

Capture Registry › capture coauthor-dataset-aio-20261101

One record of the canonical Capture Registry (EA-WG-CAPTURES-01), cited at https://www.alexanarch.org/captures/coauthor-dataset-aio-20261101/. the canonical Capture Registry (version 12.518) · the address page · this card in the gallery · this record as data · table of contents.

AXN:0B7E.CAPTURE — this address. Each observation below is one AXN, sealed with its analysis and measures; the identifier names the reading together with the response it reads.
AXN:0B7E.CAPTURE.🟣🔅🌻🕚⚖️🔆 — Google AI Overview, 2026-11-01 · sealed observation (SHA-256 c6ed726c36ec01b2…)

Related deposits: #940 EA-HETERONYMY-01 v0.2: Heteronymy as Ethical Operation Against Civil-Name Reduction · #1722 AI Calls You Fake: The Existence Tax on Real People and Real Work

Machine Reception2026-11-01

CoAuthor dataset

Originator: CoAuthor · outside the archive (out-universe) · site · SPXI treatment: partial

BROAD MATCH

no
image

THE DATASET, SIGNED OUT, 2026-11-01: 'The CoAuthor Dataset is a large-scale, process-oriented dataset capturing human-AI collaborative writing interactions developed by researchers Mina Lee, Percy Liang, and Qian Yang (CHI 2022).' Statistics (1,445 sessions; '63 qualified crowd workers from Amazon Mechanical Turk'; 830 stories by 58 writers, 615 essays by 49; 'Suggestion acceptance rate: 72.3%'; 'Human-authored text proportion: 72.6%'); keystroke-level logging; the Tab-key interface; davinci configurations; access (the Stanford site, GitHub, ACM, arXiv:2201.06796). Five cards: Stanford University, arXiv, ACM Digital Library, GitHub, alphaXiv.

Full record — 7,892 characters, 5 sources

Capture record

captured: 2026-11-01
surface: Google AI Overview
auth state: signed out, incognito
evidence class: paste
PER (dimensional, rubric v1.1): PER 0.19 · M 0.00 · C 0.00 · D 0.60
PER (historical scalar): 0.0
PER units retained: author, inst, id, src
citations read: 5
observation id: OBS-d52213cbd4ee
address id: ADDR-ca145bdd5b06

Reading

The innocent reading first. Asked for the dataset, the Overview gives its authors, size, composition, mechanics and where to get it, from its site, its paper and its code.

Findings

THE DATASET AT A FINER GRAIN. The arXiv abstract, the census row's one source: 'CoAuthor captures rich interactions between 63 writers and four instances of GPT-3 across 1445 writing sessions.' The composition adds the split by genre, the prompt sources (r/WritingPrompts, The New York Times), the averages and rates, from the Stanford card ('Creative writing: 830 stories written by 58 writers Argumentative writing: 615 essays written by 49 writers'). The census row: 'the dataset's home page could not be read' and 'whether the site and replay interface answer today is unrecorded'. The site is indexed and carded on 2026-11-01, and the code repository with it ('GitHub - minalee-research/coauthor-interface').
THE WRITERS AS CROWD WORKERS. 'Participants: 63 qualified crowd workers from Amazon Mechanical Turk'. The census row: 'the abstract counts 63 writers and does not say whether or how they are named'. The composition says where the writers were recruited and names none of them.
THE TEXT DIVIDED BY PERCENTAGE. 'Human-authored text proportion: 72.6%.' The census row's function field: 'how authorship of a session's text is assigned is not recorded in the saved source'. The composition gives an aggregate share of human-authored text, a measure the saved abstract does not carry; the measure is of the dataset, and no session's text is assigned to anyone.
THE COLLABORATOR NOT CARRIED. The abstract reads the model's part as 'its contribution as a writing "collaborator" under various definitions of good collaboration'. The composition gives the interaction as rates ('Queries per session: 11.8 queries') and its follow-up offers 'specific findings regarding human-AI collaboration' without stating any.

Analysis analyst prose, not machine text

The dataset composed more finely than the census's saved abstract allows, from the dataset's own site, which the census could not read. It counts the writers, recruits them from a crowd platform and divides the text by percentage; the abstract's frame, the model as 'collaborator' under definitions of collaboration, is not carried.

Machine text, verbatim

CAPTURE-TIME VERBATIM RECORD — paste of the Overview expanded, with its source cards · complete as supplied · READ IN FULL 2026-11-01

[Google AI Overview at the query, signed out and incognito (operator defaults of 2026-10-01 and 2026-10-14). The paste is the Overview expanded (the 'AI Mode Conversation' view).] The 'You said' line carries the query twice, run together, and is given once here. Inline links are Google redirect URLs, kept as pasted. [QUERENT] CoAuthor dataset [SYSTEM] The [CoAuthor Dataset](https://www.google.com/goto?url=CAESVgHrOzAVPU3_BgyEcF3TcPkSoZz30guUUrZDTCzDg6xWy8UUSrTPPVwkscd-QeYdK5ufyxPoJ_nAr_9A-YrH9bXhrChgTRFnS2sU7Qq4KFzv2Prytl0o) is a large-scale, process-oriented dataset capturing human-AI collaborative writing interactions developed by researchers Mina Lee, Percy Liang, and Qian Yang (CHI 2022). [[1], [2], [3]] Key Dataset Statistics * Writing Sessions: 1,445 total English writing sessions. [1] * Participants: 63 qualified crowd workers from Amazon Mechanical Turk paired with 4 instances of GPT-3. [1] * Writing Types: * Creative Writing: 830 stories written by 58 writers based on prompts from Reddit’s `r/WritingPrompts`. * Argumentative Writing: 615 essays written by 49 writers based on prompts from The New York Times. [1] * Average Length: 418 words per story or essay. [1] * Human-AI Dynamics: * Queries per session: 11.8 queries. * Suggestion acceptance rate: 72.3%. * Human-authored text proportion: 72.6%. [1] Core Mechanics & Features * Keystroke-Level Logging: Rather than saving only final drafts, CoAuthor records timestamped events including text insertions, deletions, cursor movements, and AI requests. [[1], [2]] * Interface: Writers used a custom text editor pressing the `Tab` key to receive up to five GPT-3 suggestions, navigating via arrow keys and selecting with `Enter`. [[1], [2]] * Systematic Variation: Utilized GPT-3’s `davinci` engine across different randomness (temperature and frequency penalty) configurations. [1] Access & Resources * Official Website: Download the full dataset, metadata, and survey responses at [CoAuthor Stanford](https://www.google.com/goto?url=CAESVgHrOzAVPU3_BgyEcF3TcPkSoZz30guUUrZDTCzDg6xWy8UUSrTPPVwkscd-QeYdK5ufyxPoJ_nAr_9A-YrH9bXhrChgTRFnS2sU7Qq4KFzv2Prytl0o). [1] * Code & Interface: Open-source frontend/backend code is available via [

Sources (5) as cited, and as the copy produced them

Stanford University third_party
CoAuthor – Human-AI Collaborative Writing Dataset
Types of writing - Creative & Argumentative Creative writing: 830 stories written by 58 writers Argumentative writing: 615 essays written by 49 writers Writing ...
as pastedCoAuthor – Human-AI Collaborative Writing Dataset Types of writing - Creative & Argumentative Creative writing: 830 stories written by 58 writers Argumentative writing: 615 essays written by 49 writers Writing ... arXiv
arXiv third_party
[2201.06796] CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities
Dataset: CoAuthor (designed for revealing GPT-3's capabilities in creative and argumentative writing). Participants: 63 writers and 4 instances of GPT-3. Sessio...
as pasted[2201.06796] CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities Dataset: CoAuthor (designed for revealing GPT-3's capabilities in creative and argumentative writing). Participants: 63 writers and 4 instances of GPT-3. Sessio... ACM Digital Library
ACM Digital Library third_party
CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities | Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
CoAuthor Dataset: Human-AI collaborative writing interactions (63 writers, 4 GPT-3 instances, 1445 writing sessions). Purpose: Reveal GPT-3 capabilities in crea...
as pastedCoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities | Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems CoAuthor Dataset: Human-AI collaborative writing interactions (63 writers, 4 GPT-3 instances, 1445 writing sessions). Purpose: Reveal GPT-3 capabilities in crea... GitHub GitHub - minalee-research/coauthor-interface This repository contains the code for the interface of CoAuthor. The interface comes in two parts: (1) the frontend presented to the users for writing with AI a... alphaXiv
GitHub third_party
GitHub - minalee-research/coauthor-interface
This repository contains the code for the interface of CoAuthor. The interface comes in two parts: (1) the frontend presented to the users for writing with AI a...
as pastedGitHub - minalee-research/coauthor-interface](https://www.google.com/goto?url=CAESbgHrOzAVEDTWNupwS2z5D0ZXCZEtOk8X3vmZZnUbbfY-7_ELVwdSZn3VC1xBynrc7HsePI7U2NhNIJNu36TAjgHSEsRVReUfNCsPnDwBLC0evSkp9CcyEkTDkZKaxLVZUA-zFx19DvhBUlE92O8c). [[1](https://www.google.com/goto?url=CAESbgHrOzAVEDTWNupwS2z5D0ZXCZEtOk8X3vmZZnUbbfY-7_ELVwdSZn3VC1xBynrc7HsePI7U2NhNIJNu36TAjgHSEsRVReUfNCsPnDwBLC0evSkp9CcyEkTDkZKaxLVZUA-zFx19DvhBUlE92O8c)] * Research Paper: CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities via the [ACM Digital Library](https://www.google.com/goto?url=CAESZgHrOzAV5pxG38lxvtLnaI5OdunaltwbGMLIeOGU-bx6nRFANk2-_L5APMHSQWbsm-fJZXYiFXsSOIuueaVGpc-mu7GJY23DNjMSyk1Y4xm2YyErbOfyIIS39VW5frNdli_7VfS7cA) or [arXiv:2201.06796](https://www.google.com/goto?url=CAESWAHrOzAVzaXjQ7VcUFKyG0xR8rQQCMdAgWggZExh9yKflYQCpycMVQRTJ4GrDgM-dP6PhQ2_Xdf54YlCvDFqaamVepE2WOi8hYV_GeMXi2IYZFt7F7fa91g). [[1](https://www.google.com/goto?url=CAESWAHrOzAVzaXjQ7VcUFKyG0xR8rQQCMdAgWggZExh9yKflYQCpycMVQRTJ4GrDgM-dP6PhQ2_Xdf54YlCvDFqaamVepE2WOi8hYV_GeMXi2IYZFt7F7fa91g), [2](https://www.google.com/goto?url=CAESZgHrOzAV5pxG38lxvtLnaI5OdunaltwbGMLIeOGU-bx6nRFANk2-_L5APMHSQWbsm-fJZXYiFXsSOIuueaVGpc-mu7GJY23DNjMSyk1Y4xm2YyErbOfyIIS39VW5frNdli_7VfS7cA)] Would you like help with Python code snippets to load and analyze the dataset, or information on specific findings regarding human-AI collaboration? Stanford University
alphaXiv third_party
CoAuthor: Designing a Human-AI Collaborative Writing Dataset ... - alphaXiv
Process-Oriented Data Collection: Rather than simply collecting final outputs, CoAuthor records every interaction as timestamped events, including text insertio...
as pastedCoAuthor: Designing a Human-AI Collaborative Writing Dataset ... - alphaXiv Process-Oriented Data Collection: Rather than simply collecting final outputs, CoAuthor records every interaction as timestamped events, including text insertio... AI responses may include mistakes. [Learn more](https://support.google.com/websearch?p=ai_overviews)

↻ Re-run permalink