How to Use AI in Qualitative Coding Responsibly

AI can generate plausible qualitative codes quickly. The methodological challenge is deciding which suggestions, if any, deserve a place in the analysis.

Use AI as assistance rather than authority. A suggested code should be checked against the original passage, surrounding context, current codebook and alternative interpretations.

AI can over-abstract. A participant who says they stayed because cattle could not be moved might be labelled RESISTANCE_TO_EVACUATION when the evidence points more directly to a livelihood constraint.

It can also stay too descriptive, producing topic labels that do little analytical work.

Keep researcher decisions visible. Useful distinctions include whether a suggestion was accepted, rejected, revised or deferred. Detailed documentation is most useful where the decision materially affects the framework.

Do not use AI to manufacture consensus between human coders. Disagreement may reveal ambiguity or a conceptual problem that deserves discussion.

Multilingual evidence requires additional caution because translation and coding can introduce two layers of interpretation.

AI does not solve sampling problems, remove researcher bias or establish validity. It can surface possibilities, retrieve material and assist comparison where the method permits.

Before processing research data, also consider consent, ethics, confidentiality, institutional policy, contracts and provider data handling.

The researcher should ultimately be able to defend the final code from the evidence without relying on the authority of the AI system.