Exploring the use of ChatGPT to analyze student course evaluation comments - Kathryn A. Fuller; Kathryn A. Morbitzer; Jacqueline M. Zeeman; Adam M. Persky; Amanda C. Savage; Jacqueline E. McLaughlin (BMC Medical Education, 2024)
Abstract Background Since 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. Methods Course 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. Results There 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. Conclusions This 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.