AI for Qualitative Coding: Using It on Interviews and Surveys Without Losing Interpretive Authority

What AI coding tools actually do

“AI coding” covers three quite different things, and a lot of the online argument comes from people meaning different ones.

• General chatbots such as ChatGPT or Claude. You paste text and ask for codes or themes. Flexible, but the output depends heavily on your prompt, long transcripts get cut off, and nothing links a code back to the passage it came from unless you ask. Our guide to whether ChatGPT can do thematic analysis covers this route.
• AI features inside CAQDAS software such as NVivo, ATLAS.ti and MAXQDA's AI Assist. These suggest codes or summaries inside a project you are already coding by hand.
• Purpose-built analysis tools such as thematicanalysis.ai that take your transcripts or survey export, produce codes with definitions and supporting quotes, and group them following a named method.

All three work the same way underneath. A large language model reads the text and predicts which labels best describe each passage, based on patterns in the enormous amount of text it was trained on. It does not know your participants, your field or your research question beyond what you tell it. That is the root of both its usefulness (it has seen a lot of language) and its limits (it has seen very little of your context).

Using AI to code interview transcripts

Interviews are where the stakes are highest, because the meaning is often in what people imply, hesitate over or come back to twenty minutes later. A workflow that keeps you in charge looks like this:

1. Read before anything is coded. Read all your transcripts yourself, or at the very least a good sample, before you see any AI output. Familiarisation is the closeness the hand-coders are defending, and no tool does it for you.
2. Code one or two transcripts by hand first. This gives you your own sense of what matters, and a benchmark to judge the AI against. If you are new to coding, our guide to coding an interview transcript walks through it.
3. Run the AI pass. Ask for codes with definitions and the exact quote behind each one. A code without its quote can't be checked.
4. Audit line by line. For every code, read the passage. Keep it, rename it, move it, or delete it. Then read the transcript for passages the AI skipped.
5. Compare with your hand-coded transcripts. Where you disagree with the AI, you have learned something about your data or about the tool. Write it down.
6. Build the themes yourself. AI suggestions for groupings can be a prompt to think, but deciding what the patterns mean, and what story they tell about your research question, is the analysis.

Umer and colleagues (2026) used almost exactly this workflow on Roman Urdu interviews with people with bipolar disorder and their carers. ChatGPT produced structured codes quickly, but a bilingual researcher auditing every code line by line found it flattened emotional and cultural meaning. It read a phrase about caring for the sick simply as “patient care,” missing the unspoken expectations placed on daughters-in-law. The AI saved time. The researcher supplied the analysis.

Using AI to code open-ended survey responses

Open-ended survey answers are a better fit for AI assistance, for two reasons. The volume is often large (hundreds or thousands of responses), and each response is short, so the task is closer to sorting content into categories than to interpreting a life story. Even the critics draw this distinction. Jowsey et al. (2025) contrast reflexive thematic analysis with word-counting forms of content analysis, which they note can be automated.

The risk with surveys is different: AI tends to amplify the common answer. The letter from Jowsey et al. warns that models are predisposed to reproduce dominant language and patterns, which risks quieting marginal voices. In a survey that can mean the one response that reframes the whole question gets folded into a generic category. So for surveys, spend your review time on the outliers and on responses the AI placed in catch-all categories like “other” or “general comments.” Our guide to analysing open-ended survey responses covers the rest of the process.

thematicanalysis.ai was built for exactly this kind of review. It drafts codes from your interview transcripts or survey responses, and every code comes with a definition and the verbatim quote it came from, so you can check each one against the data before it goes anywhere near your findings. You edit, move or remove codes and rename themes, and the interpretation stays with you. If you want to see how its first pass compares with your own, the first three participants are free.

What the research shows

The studies comparing AI and human coding point the same way more often than the online debate suggests.

Two findings recur. First, AI is reliable on what the text says and unreliable on what it means. Second, the best results come when a person and a model each do what they are good at. Barany and colleagues found the fully human codebook missed some themes the hybrid approaches caught, and the fully automated one was the outlier on every measure. For a wider look at validity, see whether AI thematic analysis is valid for academic research.

The case against: interpretive authority

The Reddit thread's worry has a serious academic version. In December 2025, Qualitative Inquiry published an open letter from Tanisha Jowsey, Virginia Braun, Victoria Clarke, Deborah Lupton and Michelle Fine, endorsed by 419 experienced qualitative researchers from 32 countries, rejecting generative AI for reflexive qualitative research. Their reasons are worth stating in full, because they are stronger than “AI makes mistakes.”

• AI cannot make meaning. A language model predicts plausible text. It does not understand the world or the data, so at best it simulates analysis. Reflexive analysis is meaning-making, so it cannot be done by something that does not make meaning.
• Qualitative research is a human practice. It is done by people, with people, for people. The authors argue that AI is inappropriate at every phase of reflexive analysis, including initial coding, because coding is already interpretation.
• The harms are real. The environmental cost of data centres and the exploitation of data workers, often in the Global South, are ethical reasons to refuse, separate from the methodological ones.

Two further points deserve attention. The letter notes that even researchers who keep a human “in the loop” warn that wanting the AI to be reliable weakens our ability to critique it. That is automation bias, and it is the most practical risk for a tired student at midnight. Nguyen and Welch (2025), writing in Organizational Research Methods, add that uncritical use brings epistemic risks, because the essence of qualitative analysis is interpreting meaning, which they see as a human capability.

And the hand-coders' core claim is true. Working through every line is one of the ways researchers come to know their data. If a tool lets you skip reading your transcripts, you will know them less well, and it will show in your analysis and your viva.

The counter-argument

The letter drew published replies. Susanne Friese answered it directly in 2025, and in 2026 Friese, Kien Nguyen-Trung, Steve Powell and David Morgan followed with Beyond Binary Positions in Qualitative Inquiry. They accept the ethical concerns but contest the conclusion that AI is inherently incompatible with meaning-based research. Drawing on those replies, the evidence above and our own view, the case for researcher-led use runs like this.

• The AI doesn't make meaning. The researcher does. The model proposes; the researcher decides. In Friese's 2025 reply, meaning emerges from the researcher working with the data and the tool, with the researcher holding interpretive authority throughout.
• It is scaffolding, like tools we already accept. CAQDAS software, memos, diagrams and a colleague's second opinion all shape analysis without taking it over. Friese and colleagues argue that, used critically and under close researcher leadership, AI can sit in the same category.
• Hand coding does not guarantee closeness. This one is our view rather than the papers'. Coding transcript eighteen of twenty at 1am is not intimacy with the data, it is endurance. Closeness comes from reading, re-reading, memoing and arguing with your own interpretation. A tool that removes clerical work can leave more time for that.
• The evidence favours hybrids, not either extreme. The studies above keep finding that human review of AI output does better than AI alone, and in some cases catches things humans alone missed.
• Abstinence is not the only ethical response. Friese argues that environmental and labour concerns call for responsible use and stronger regulation, not blanket refusal. You may or may not agree, but it is a position you can defend.

Where does that leave the Reddit thread? Both sides agree on more than it looks. The critics are right that AI cannot interpret and that uncritical use produces thin analysis. The defenders are right that a researcher who reads the data, audits every code and builds the themes themselves has not handed over interpretive authority. The disagreement that remains is about whether initial coding is already interpretation, and for reflexive approaches, that is a real methodological question you should settle with your supervisor rather than with a tool vendor.

Where the line sits

A practical way to split the work, for methods where AI assistance is acceptable:

Seven rules for keeping interpretive authority

1. Read your data before the AI does. Familiarisation is not optional, and it is the part hand-coders are right to protect.
2. Never accept a code without reading its quote. If a tool can't show you the passage behind a code, don't use that code.
3. Code a sample by hand and compare. One or two transcripts is enough to calibrate how far to trust the tool on your data.
4. Disagree on purpose. For each theme the AI suggests, ask what it leaves out and whose voice it smooths over. Look hardest at emotion, irony, culture and silence.
5. Write memos about your changes. A record of what you kept, rewrote and rejected is your audit trail, and it is evidence of your interpretive work. Our guide to reflexivity explains how to use it.
6. Build the themes yourself. Treat AI groupings as prompts, not findings. The difference between a code and a theme is where your analysis lives.
7. Disclose exactly what you did. Name the tool, the stage, and how you checked it. Our guide to reporting AI-assisted analysis has wording for your methods section.

Which analysis methods it fits

If you haven't chosen a method yet, our overview of thematic analysis frameworks and our comparison of thematic analysis software can help.

Frequently asked questions