A Pilot Study on Generative Artificial Intelligence’s Reliability in Qualitative Research Quality Appraisal Using CASP and JBI Checklists
Bibliographic record
Abstract
Generative artificial intelligence (genAI) tools are transforming workflows, with growing interest in their potential applications in qualitative research. While the use of genAI in facilitating the systematic review process has been explored, its application in the quality appraisal of qualitative research remains to be understood. This pilot study aims to evaluate the degree to which ChatGPT appraises qualitative research using popular appraisal tools compared to human assessments. Two reviewers applied the Critical Appraisal Skills Program (CASP) and Joanna Briggs Institute (JBI) checklists for qualitative research to studies identified through a previously published review (n = 21). Next, iteratively developed prompts along with a copy of each study were uploaded to ChatGPT to instruct it to appraise each article. Interrater reliability measures and crude agreements were conducted to estimate the level of agreement between human and genAI assessments. Interrater reliability assessments between human and ChatGPT (GPT-5) revealed no agreement to moderate agreement for CASP checklist items (kappa: <.00-.46; crude agreement: 23.8%-100%) and from none to substantial for JBI items (kappa: <.00-.83; crude agreement: 4.8%-95.2%). Agreement was highest for reporting-based elements such as study aims, ethics approval, value of research (CASP), and participant voices and conclusions (JBI). Disagreements were greatest for interpretive and context-dependent items such as research design, researcher-participant relationships, and worldview-methodology congruity. Findings demonstrate that ChatGPT (GPT-5) can reliably identify objective components yet performs inconsistently when assessing items requiring nuance and contextual understanding across both checklists. Currently, any adoption of genAI for quality appraisal of qualitative research must be carefully applied only alongside human assessments and uphold principles of transparency and data privacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.425 | 0.552 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".