Reliability of ChatGPT in automated essay scoring for dental undergraduate examinations.

Autor: Quah, Bernadette, Zheng, Lei, Sng, Timothy Jie Han, Yong, Chee Weng, Islam, Intekhab
Předmět:
Zdroj: BMC Medical Education; 9/3/2024, Vol. 24 Issue 1, p1-12, 12p
Abstrakt: Background: This study aimed to answer the research question: How reliable is ChatGPT in automated essay scoring (AES) for oral and maxillofacial surgery (OMS) examinations for dental undergraduate students compared to human assessors? Methods: Sixty-nine undergraduate dental students participated in a closed-book examination comprising two essays at the National University of Singapore. Using pre-created assessment rubrics, three assessors independently performed manual essay scoring, while one separate assessor performed AES using ChatGPT (GPT-4). Data analyses were performed using the intraclass correlation coefficient and Cronbach's α to evaluate the reliability and inter-rater agreement of the test scores among all assessors. The mean scores of manual versus automated scoring were evaluated for similarity and correlations. Results: A strong correlation was observed for Question 1 (r = 0.752–0.848, p < 0.001) and a moderate correlation was observed between AES and all manual scorers for Question 2 (r = 0.527–0.571, p < 0.001). Intraclass correlation coefficients of 0.794–0.858 indicated excellent inter-rater agreement, and Cronbach's α of 0.881–0.932 indicated high reliability. For Question 1, the mean AES scores were similar to those for manual scoring (p > 0.05), and there was a strong correlation between AES and manual scores (r = 0.829, p < 0.001). For Question 2, AES scores were significantly lower than manual scores (p < 0.001), and there was a moderate correlation between AES and manual scores (r = 0.599, p < 0.001). Conclusion: This study shows the potential of ChatGPT for essay marking. However, an appropriate rubric design is essential for optimal reliability. With further validation, the ChatGPT has the potential to aid students in self-assessment or large-scale marking automated processes. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index