Awareness and Attitudes Toward Artificial Intelligence Language Generation Models in Medical Education: A Cross-Sectional Questionnaire Study Among Medical Students in Southern China.

Purpose To evaluate the feasibility of artificial intelligence language generation models (AILMs) in medical education, we examined the utilization patterns and attitudes of medical students in a developed area of Southern China. Methods We conducted a cross-sectional questionnaire survey assessing educational background, awareness, usage, and attitudes towards AILMs. Attitudes were measured using a five-point Likert scale, where scores of 4 or above indicated support, scores of 2 or below indicated opposition, and a score of 3 indicated a neutral stance. Results Among the 254 respondents, the average awareness score for AILMs was 2.4. AILMs were primarily used for solving medical and academic problems. Although students were aware of many domestic AILM products, foreign products were preferred. More than half of the students used AILMs less than once a week, and 13 (5.1%) students had never used them. A significant portion supported the integration of AILMs in current (187/254, 73.6%) and future (194/249, 78.0%) education, with a strong correlation between these attitudes (² = 46.351, P < 0.001). Concerns about technological immaturity were a major reason for opposition. A higher proportion of those who opposed the use of AILM had advanced computer skills compared to those with lack of or basic computer skills (10/47, 13.5% vs. 9/177, 5.1%, P = 0.010). Even after adjusting for specialty and academic performance, advanced computer skills were independently linked to opposition (OR 2.959, 95% CI 1.109 - 7.898). Conclusion While medical students generally support the use of AILMs, broader acceptance requires addressing challenges such as enhancing the quality and promotion of domestic AILMs and considering the diverse perspectives of individuals with varying levels of computer proficiency.