ArticleBMC medical education2024
Exploring the use of ChatGPT to analyze student course evaluation comments.
Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Faculty professional development and career advancement through Student Evaluations of Teaching (SETs).BMC proceedings · 2026Article
- The use and methodological reporting of large language models in qualitative research: a scoping review.BMC medical research methodology · 2026Article
- ChatGPT in medical education: a cross-sectional analysis of usage, attitudes, perceptions, and practices among Saudi medical students.Frontiers in artificial intelligence · 2026Article
- Student perceptions of GenAI as a virtual tutor to support collaborative research training for health professionals.BMC medical education · 2025Article
- Using ChatGPT for medical education: the technical perspective.BMC medical education · 2025Article
- Application of Artificial Intelligence Generated Content in Medical Examinations.Advances in medical education and practice · 2025Article
- Comparing GPT-4 and Human Researchers in Health Care Data Analysis: Qualitative Description Study.Journal of medical Internet research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince the release of ChatGPT, numerous positive applications for this artificial intelligence (AI) tool in higher education have emerged. Faculty can reduce workload by implementing the use of AI. While course evaluations are a common tool used across higher education, the process of identifying useful information from multiple open-ended comments is often time consuming. The purpose of this study was to explore the use of ChatGPT in analyzing course evaluation comments, including the time required to generate themes and the level of agreement between instructor-identified and AI-identified themes.
methodsCourse instructors independently analyzed open-ended student course evaluation comments. Five prompts were provided to guide the coding process. Instructors were asked to note the time required to complete the analysis, the general process they used, and how they felt during their analysis. Student comments were also analyzed through two independent Open-AI ChatGPT user accounts. Thematic analysis was used to analyze the themes generated by instructors and ChatGPT. Percent agreement between the instructor and ChatGPT themes were calculated for each prompt, along with an overall agreement statistic between the instructor and two ChatGPT themes.
resultsThere was high agreement between the instructor and ChatGPT results. The highest agreement was for course-related topics (range 0.71-0.82) and lowest agreement was for weaknesses of the course (range 0.53-0.81). For all prompts except themes related to student experience, the two ChatGPT accounts demonstrated higher agreement with one another than with the instructors. On average, instructors took 27.50 ± 15.00 min to analyze their data (range 20-50). The ChatGPT users took 10.50 ± 1.00 min (range 10-12) and 12.50 ± 2.89 min (range 10-15) to analyze the data. In relation to reviewing and analyzing their own open-ended course evaluations, instructors reported feeling anxiety prior to the process, satisfaction during the process, and frustration related to findings.
conclusionsThis study offers valuable insights into the potential of ChatGPT as a tool for analyzing open-ended student course evaluation comments in health professions education. However, it is crucial to ensure ChatGPT is used as a tool to assist with the analysis and to avoid relying solely on its outputs for conclusions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.