Can Generative AI Perform Thematic Analysis?

Can You Ask Generative AI to Conduct Thematic Analysis of Your Interviews?

You have collected interview transcripts and decided to use thematic analysis. Instead of reading every transcript repeatedly, developing codes, and gradually constructing themes, you consider uploading the material to ChatGPT or another generative AI system.

You ask it to identify themes. Within seconds, it returns a table containing codes, categories, theme names, and supporting quotations. The results look organized and may even resemble the findings section of a published qualitative study.

But has thematic analysis actually taken place?

The answer depends partly on what you mean by thematic analysis. Different approaches assign different roles to coding, researcher interpretation, theoretical assumptions, and analytical consistency. A system that groups similar statements may accomplish a useful task without satisfying the methodological requirements of the approach you intended to use.

AI Can Perform Thematic Analysis Tasks, but That Is Not the Same as Completing a Defensible Thematic Analysis

What It Means for AI to Perform Thematic Analysis

Not All Thematic Analysis Approaches Work the Same Way

Thematic analysis is a family of approaches for examining patterns of meaning across qualitative data. It is not a single standardized procedure that can be reproduced simply by following a universal sequence of commands.

Braun and Clarke distinguish among different orientations to thematic analysis, including coding reliability approaches, codebook approaches, and reflexive thematic analysis. These orientations differ in their assumptions about coding, the researcher's role, and how analytical quality should be evaluated.

These are broad methodological orientations, not rigid classifications of every published study. Researchers should identify the specific approach they are using before deciding what analytical tasks to delegate.

In particular, the requirements of reflexive thematic analysis should not be assumed to be interchangeable with those of a coding reliability approach.

Can AI Follow Braun and Clarke's Six Phases?

Braun and Clarke's widely used approach describes six phases of thematic analysis. These phases are recursive rather than a rigid sequence in which one stage must be completed permanently before the next begins.

AI can contribute to activities associated with each phase, but its contribution must be distinguished from the researcher's methodological work.

The table illustrates possible forms of assistance rather than establishing that AI independently completes each phase. In reflexive thematic analysis, the phases are part of an evolving interpretive process, not simply operations performed on a dataset.

What Is the Difference Between a Topic, a Code, and a Theme?

One of the most consequential problems with AI-generated thematic analysis is the tendency to treat recurring topics as fully developed themes.

Consider interviews about students' experiences of online examinations.

The topic identifies what participants discussed. The code identifies a potentially meaningful feature of an account. The candidate theme advances an interpretive proposition about how technological conditions shape students' experience of assessment.

That proposition would need to be supported by the broader dataset. A sophisticated theme name is not evidence of analytical quality by itself.

AI may help develop possible themes from interview transcripts, but the researcher must determine whether those themes represent meaningful patterns rather than convenient headings.

Can AI Conduct Inductive and Deductive Thematic Analysis?

Generative AI can support both inductive and deductive analytical activities, although the nature of the task differs.

In a deductive approach, analysis is informed by an existing theoretical framework, research question, or predefined coding structure. AI may be asked to classify excerpts according to explicit code definitions. Such tasks can be evaluated against a human-reviewed reference set, although agreement alone does not establish the overall quality of the analysis.

In an inductive approach, codes and patterns are developed through engagement with the data rather than determined entirely in advance. AI can propose candidate codes or patterns, but its suggestions are shaped by learned language regularities, prompt instructions, and the context supplied.

It would therefore be misleading to assume that AI performs interpretation without prior assumptions simply because the researcher did not provide a codebook.

In practice, many qualitative studies combine inductive and deductive elements. Researchers should describe their actual analytical decisions rather than using these labels as guarantees of methodological purity.

What Does Research Show About AI-Generated Themes?

Empirical research indicates that large language models can generate outputs resembling those produced through human qualitative analysis. However, performance varies according to the analytical task, dataset, model, and evaluation criteria.

Comparative research in healthcare has examined the similarity between themes generated by large language models and themes produced through conventional human analysis. Some findings suggest substantial overlap for particular datasets, while other evaluations identify difficulties with contextual interpretation, inductive reasoning, and the selection of supporting evidence.

Such findings should be interpreted within the designs of the individual evaluations. Similarity between theme labels does not necessarily establish equivalent analytical depth. Conversely, disagreement does not automatically mean that one interpretation is invalid, particularly within interpretive approaches that allow multiple defensible readings.

A further methodological difficulty concerns the standard against which AI output is evaluated. If the human analysis is treated as the single correct answer, the evaluation may overlook the interpretive plurality recognized in some qualitative traditions.

Researchers should therefore distinguish performance on a defined coding benchmark from the broader question of whether an AI-generated thematic account is theoretically and methodologically persuasive.

Why AI May Produce Superficial or Overly Coherent Themes

Generative AI is capable of organizing diverse statements into a concise and coherent narrative. That ability is useful for summarization, but it can also create problems for thematic analysis.

A model may privilege frequently expressed ideas, group together accounts that differ in important ways, or produce familiar categories such as "Challenges," "Benefits," and "Recommendations."

It may also overlook participants whose experiences complicate the dominant interpretation.

For example, most students may describe recorded lectures as increasing flexibility, while others experience recordings as additional work because they lack uninterrupted study time. A theme such as "Flexible Learning Opportunities" may conceal the conditions under which flexibility is actually available.

Actively searching for contradictory or deviant cases can help researchers test whether an emerging interpretation adequately represents the dataset.

Can AI Interpret Latent Meaning?

Thematic analysis may examine semantic meaning, which is relatively explicit in participants' accounts, or latent meaning, which concerns underlying assumptions, ideas, and conceptualizations.

AI can generate interpretations at either level. However, producing a latent interpretation does not establish that it is grounded in the participant's circumstances or supported by the analytical framework.

Suppose a participant says, "I don't ask questions during online classes because I don't want to waste everyone's time."

A semantic code might be "hesitation to ask questions." A possible latent interpretation might concern the participant's understanding of legitimate classroom participation or their perceived obligation not to inconvenience others.

Other readings are possible. The researcher must consider the surrounding interview, institutional context, and wider dataset before treating one interpretation as analytically justified.

These limitations become especially consequential when AI encounters culturally specific expressions, ambiguity, or irony.

Does AI Change the Researcher's Role in Reflexive Thematic Analysis?

Reflexive thematic analysis recognizes the researcher as actively involved in producing knowledge through engagement with the data. Themes are not understood as fixed objects waiting to be extracted independently of the analytical process.

AI may suggest alternative readings, organize extracts, or prompt the researcher to examine assumptions. However, assigning it responsibility for determining the final themes raises questions about how interpretation, researcher subjectivity, and accountability are understood.

A model can produce a positionality statement or describe possible biases, but such text should not be mistaken for the researcher's own reflexive engagement.

The distinction between generating reflexive language and exercising reflexivity is particularly important when deciding whether AI assistance is compatible with the study's methodology.

What Ethical Conditions Must Be Met?

Interview transcripts may contain sensitive personal information, indirect identifiers, and contextual details that make participants recognizable. Before submitting them to an AI service, researchers should verify whether the proposed processing is permitted under participant consent, ethics approval, institutional policy, data-use agreements, and applicable privacy requirements.

The service's data retention, access controls, processing arrangements, and model-training terms should also be reviewed. Removing names does not necessarily eliminate re-identification risks.

Researchers should additionally verify that AI-generated quotations reproduce authentic transcript material. A plausible quotation that was never spoken by a participant is not qualitative evidence.

From AI-Generated Topics to a Defensible Thematic Interpretation

What an Initial AI Response Might Look Like

Asked to identify themes, an AI system might produce:

• Theme 1: Privacy Concerns
• Theme 2: Student Engagement
• Theme 3: Communication Preferences
• Theme 4: Technical Challenges
• Theme 5: Social Connection

These labels describe topics represented in the excerpts. They may be useful for preliminary organization, but they do not yet communicate a developed interpretive account.

How the Researcher Could Develop a More Analytical Interpretation

Rather than treating the five topics as final themes, the researcher considers how the accounts relate to one another.

Participants A and B raise questions about the social consequences of visibility. Participant D introduces the possibility that camera use is constrained by technology rather than willingness to participate. Participant C describes a communication channel that supports participation without requiring immediate verbal expression. Participant E complicates any assumption that visibility is universally uncomfortable.

One possible candidate theme is:

This interpretation is more conceptually developed than a list of topics, but it remains provisional. Five illustrative excerpts are insufficient to establish that it represents a meaningful pattern across a complete dataset.

How the Researcher Should Proceed

The example demonstrates that AI can contribute material for thematic analysis without independently establishing the meaning of that material.

Common Mistakes When Using AI for Thematic Analysis

How to Incorporate AI Into Thematic Analysis Without Compromising the Method

Start by identifying the thematic analysis approach you intend to use. Then determine which activities AI can reasonably support and which require your own interpretive engagement.

There is no universally appropriate level of AI involvement. A structured codebook project may justify automated classification with validation, while a reflexive thematic analysis may use AI more cautiously as a source of alternative readings or analytical questions.

Ask AI for Analytical Possibilities Rather Than Final Themes

A more useful prompt may be:

"Using the supplied anonymized excerpts and research question, identify possible patterns of shared meaning. Distinguish descriptive topics from interpretive theme candidates. For each suggestion, identify supporting excerpts, alternative interpretations, and evidence that might challenge the proposed pattern. Do not invent quotations or present the suggestions as final themes."

This can encourage critical exploration rather than premature closure. The output still requires independent assessment.

Document How Themes Were Actually Developed

Keep records of how AI was used, which suggestions influenced the analysis, and how the researcher revised or rejected them. Depending on the study, this may include prompts, model information, transcript identifiers, analytical memos, theme revisions, and decisions concerning contradictory evidence.

Documentation should reflect the actual process rather than reconstructing a tidy sequence after the findings have already been written.

When preparing the manuscript, explain the role of AI in the analytical procedure sufficiently for readers to evaluate its methodological implications.

Before Accepting AI-Generated Themes

Frequently Asked Questions About AI Thematic Analysis

AI Can Generate Themes, but Thematic Analysis Requires More Than Theme Generation

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