Grounded Theory

Toolkit · Qualitative

A reusable AI toolkit for research using grounded-theory techniques.

What has been built

An agent skill combines methods, workflows, templates and software helpers. Analysis expanded to 22 interviews over three cycles. Contrary accounts and review led to a distinction between approval of an outcome and authorization to act.

What comes next

Extend empirical analysis and examine alternative explanations and theoretical claims.

From accounts to explanations

A working agent skill connects source passages, codes, comparisons and changing categories. The researcher sets the question and judges the explanations; Python helpers preserve the records and check their links.

  • Keep evidence in view

    Tie incidents and codes to source lines; distinguish reported actions, interpretations and unknown outcomes.

  • Learn from contrary cases

    Record what a comparison changes, why a category is revised and which rival explanations remain.

  • Specify the next question

    Propose evidence that could distinguish explanations. Sampling remains a proposal for the researcher to decide.

What the interview study changed

Across 22 interviews and three cycles, contrary accounts and review separated an acceptable outcome from authorization to act and the ability to redirect the work. These are provisional distinctions, not a validated theory.

CycleRecordsWhat changed
2CMP015, MEM006Repeated repairs followed by a product shutdown challenged the initial idea: having a correction channel does not mean correction works.
3CMP027, MEM010An investor email was sent without expected approval, yet its content was accepted. The revised category separates result, authorization and capacity to intervene.
3, reviewAUD-01, E714Review prompted a successor episode that keeps content acceptance and failed approval separate. The recipient’s response and later control remain unknown.

The repository’s worked example links each distinction to its records. Source transcripts are not included. The open sampling proposal seeks a worker’s disagreement followed through to a known consequence.

Two cycles of an invented example

The toolkit comes with a scripted example: a nine-line invented text and two cycles of analysis records, written by a script through the toolkit’s own helpers, without a model. Step through it to see what each step adds to the record.

Step 1 of 10

Prepare the cycle

The question, the method label and the source are fixed before any coding. The source is bound by its fingerprint, so a changed byte would show.

Invented exampleCycle 1 · open
Episodes
0
Incidents
0
Codes
0
Comparisons
0
Category versions
0
Memos
0
Sampling proposals
0

Invented text

  1. A maker begins a short moving title with specification A.
  2. After an unreadable result, the maker repeats specification A and accepts the next result.
  3. A maker begins another moving title with specification B.
  4. After an unreadable result, the maker switches to a static panel and records a transformed outcome.
  5. A maker begins a moving title with specification C.
  6. The maker explicitly abandons that attempt after an unreadable result.
  7. A maker begins a moving title with specification D.
  8. The recording stops while the maker is still adjusting the title; no result is visible.
  9. The maker says: I used a static panel because movement hid the label.

Wholly invented: no real person, source or finding.

Added in this step

Question
How do responses to an unreadable result differ?
Method
inductive process study using grounded-theory techniques
Source
MNF-SYNTHETIC-TRACE, normalized text (en)
Fingerprint
SHA-256 b332fbc6f594ba89…
Models used
None
Release
Not for release
Saturation
Not assessed
Step 1 of 10
Read the AI prompt

A provisional analysis of 22 interviews

The toolkit was applied to 22 podcast interviews in which people describe work they do with AI. The analysis ran in three cycles, and each cycle’s comparisons and sampling proposal guided which interviews were added next.

What follows are counts of the saved records in each cycle. No interview text appears on this page.

The study’s question How do people describe initiating, directing, evaluating, and changing their work when they use AI, and under what conditions do those capacities expand or contract?

Its sampling proposal asked for failed, discontinued or disputed uses, and for contrasts between novices and experts and between workers and owners.

Its sampling proposal asked who can obtain a consequential change when local revision stalls: specialists, providers or organizational authority.

Its sampling proposal is left open: a worker’s disagreement with AI-mediated work, or refusal of it, followed through to a known consequence. The analysed interviews hold no such account.

Cycle 3: 22 interviews

Records in the cycleCurrentVersions kept
Interviews analysed22
Account groupings8689
Incidents234
Codes234235
Comparisons43
Categories615
Memos14
Sampling proposals3

Each cycle keeps the earlier ones: the counts are cumulative, and replaced versions stay in the record.

An account grouping is the toolkit’s episode record: one reported attempt, a general account of a kind of situation, or a piece of hypothetical advice. Groupings are not independent events.

How the account groupings end
Unknown62
Accepted15
Transformed6
Postponed2
Abandoned1
What the incidents are
Actions the speaker reports129
The speaker’s own interpretation78
Claims by technology companies14
Interpretations by others13
Actions observed directly0
Kinds of account
Generalized accounts45
Bounded reported attempts40
Hypothetical advice1
Responses to contrary cases
Revise the category8
Bound it1
Retain it, with a reason1
Leave unresolved3

Download the dataCounts per cycle (CSV)Counts per cycle (JSON)The invented example’s records (JSON)

Limits and next work

  • The analysis remains provisional: no claim to saturation or a validated theory. All incidents are mediated accounts; the 86 current account groupings are not independent events, and 62 endings are unknown.
  • The sample is purposive, with founders and vendors overrepresented. Review used the producers’ model, without blinding, on selected passages; no independent human assessment or literature comparison has been completed.
  • The 50 released tests pass and the invented example runs. These establish software behavior, not correct interpretations. Broader safeguards against prompt injection, bias and leakage remain to be evaluated.
  • The transcripts are withheld because no redistribution grant has been established for the retained copies. The synthetic example is self-contained.
  • Proposed: extend empirical analysis, examine alternative explanations and assess the theoretical claims.

Run and inspect

Open appendix/05-grounded-theory for the agent skill, runnable synthetic example, 50 tests, interview-study records and worked comparison trace. The README gives setup and offline check commands.

The analytical rows retain their source bindings and versions. Checks reproduce the counts and worked trace; source verification requires the withheld transcripts.

Credits

Method sources: Boeije (2002) on purposeful constant comparison; Birks, Chapman and Francis (2008) on memoing; Foley and Timonen (2015) on theoretical sampling.

Source accounts: Lenny’s Podcast, its host Lenny Rachitsky, and the interviewees.

The interview analysis was produced by AI agents under a researcher’s direction and reviewed by a separate agent on the same model.

The toolkit’s code and agent skill are offered under the Apache License 2.0, and the project’s documents and analytical records under CC BY 4.0. The rights in the transcripts stay with their owners.