Exploring ChatGPT-Assisted Qualitative Data: Comparing Human and AI-Assisted Coding Using Braun and Clarke’s Thematic Analysis

A qualitative study compares prior human thematic coding with ChatGPT analysis of public focus-group data. Authors report partial category overlap but weaker interpretive depth in AI output.

L1Task assistance Source Peer-reviewed source Outcome Author-reported

Evidence & verification

Source level

Peer-reviewed source
Grounded in a peer-reviewed publication. This describes the source; it does not mean the reported results have been independently reproduced.

Outcome verification

Author-reported
Outcomes are reported by the team that built the system. The entry is source-checked against the team's own publications; not independently reproduced.

Sources checked

2026-11-01 (UTC)

AI source review of abstract-only. Identity/version: primary snapshot matches this queued resource; exact upstream release/byte parity is not generally verified. Source classification is not a correctness or research-benefit guarantee. Queue issued date is 2026-11-02; primary page was available and accessed on 2026-11-01; exact publication day was not corroborated. Abstract-only review; model/version references do not establish execution setup.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (1)

Research applicability

Direct evaluation of AI assistance in qualitative academic analysis.

Getting started

Read the primary methods and adapt only after checking source limitations; this is editorial guidance, not an executed recipe.

Maintenance

Source snapshot checked 2026-11-01; ongoing maintenance, installed behavior and current-version parity not generally verified.

License

Consult original licence and data/provider terms; source access is not reuse clearance. No licence/rights audit or installation was performed.

Workflow & human oversight

Scoped L1: ChatGPT performs one bounded thematic-analysis task, assessed against prior human coding (L35-L37). No AI orchestration of connected stages documented. Human control/boundary: Human coders complete six phases before AI engagement; researchers compare outputs.

Workflow level is not an evidence score; higher is not better evidence.

Approach

Direct evaluation of AI assistance in qualitative academic analysis. Documented steps: Public focus-group dataset; human coding then ChatGPT analysis and comparison.

Outcomes (as reported by the originating team)

Author reports or documentation only, not independently reproduced. No independent reproduction was performed here.

Limitations

Abstract-only, not reproduced; exact publication day unresolved. Model/version reference alone does not establish execution setup. Peer-review source class is not a quality guarantee.

Reproducibility

Primary-source text was read and preserved with provenance hashes. No candidate code, model, benchmark, data upload or claimed outcome was executed/reproduced.

Adoption requirements

Verify research scope, data rights, tool requirements and outputs before use; these editorial suggestions were not executed.

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