AI can reduce clerical effort in qualitative work, but it cannot take responsibility for the analytical decisions that make a study credible. A responsible workflow treats AI output as provisional: it can suggest a code, identify a possible comparison, summarise selected evidence, or draft a reporting paragraph. The researcher remains responsible for checking the source material, accepting or changing the suggestion, and documenting consequential decisions.
Use AI where its role is visible
AI assistance is most defensible when the input, task, and output are clear. For example: “Suggest provisional labels for these selected passages”; “compare these answers to one interview question”; or “draft a descriptive caption from this verified table.” The request should identify what AI may do and what it must not infer.
Keep evidence before prose
Do not accept a fluent paragraph merely because it sounds academic. Review the evidence pool, code definitions, project memos, and metadata context before accepting a drafted finding. If an output makes a claim that cannot be traced to source material, revise or reject it. AI is especially likely to overstate patterns when it receives a partial evidence sample or ambiguous labels.
Protect participant material
Before using any AI feature, confirm that the planned use is compatible with consent, institutional policy, data-protection duties, and any agreements with participants or data providers. Minimise identifiable information, use only the data necessary for the task, and do not assume that a technical feature resolves an ethical question.
Maintain an audit trail
Record the prompt purpose, evidence scope, accepted and rejected suggestions, substantive codebook changes, and final researcher edits. This does not require publishing every prompt. It means you can explain how an AI-assisted output became part of the analysis and show that human judgement remained active.
A review checklist
- Does the output match the selected evidence exactly enough for its claim?
- Has it confused code frequency with importance or participant prevalence?
- Has it ignored negative, ambiguous, or less frequent evidence?
- Does it use the project’s terms and methodological boundaries accurately?
- Would you make the same decision if the AI label were hidden?
Questions researchers often ask
Can I opt out of AI?
Yes. In Byleron One, AI features are optional. You can code, memo, analyse, select evidence, and write results manually.
Should AI-created codes be accepted automatically?
No. Treat them as draft proposals. Confirm the evidence range, definition, hierarchy, and usefulness before accepting them.