The roles of challenge and curiosity in deep learning and creative tendencies among Chinese medical undergraduates

Abstract

Abstract Deep learning plays a crucial role in enhancing medical students’ clinical thinking and lifelong learning competencies, however, systematic research on its status and influencing mechanisms among multidisciplinary medical students remains insufficient. This study aims to investigate the current status and influencing factors of deep learning ability in medical students to provide a basis for optimizing medical education strategies, using a cross-sectional design. An online cross-sectional survey employing random sampling was administered to 1,833 medical students from a medical university in Anhui Province, China. Following the removal of 225 incomplete or invalid questionnaires, the effective sample size was 1,608. Data were collected using the General Information Questionnaire, the Deep Learning Ability Scale for College Students, and the Williams Creativity Aptitude Test (WCAT) to investigate the deep learning and creativity tendencies of medical students. The study found that medical students’ deep learning, particularly in transfer learning, critical thinking skills, teamwork, learning to learn, and academic spirit, is significantly higher than that of non-medical students. Regarding creative tendency, the majority of medical students (72.39%) scored at the average level, with over a quarter (27.61%) demonstrating above-average creative potential. The creative tendency and its factors were positively correlated with deep learning and its factors. Notably, the high creative tendency group scored significantly higher than the low group on both the total deep learning scale and all subscales( p < 0.05). Additionally, the deep learning and creativity tendencies showed a negative correlation with household registration (urban/rural) and age. Medical students with urban household registration scored significantly higher in deep learning and creativity tendencies than those with rural household registration. The lower age group also scored significantly higher in deep learning and creativity tendencies than those in the higher age group. Using hierarchical regression analysis to control for demographic variables, challenge and curiosity were entered into the final model, and these two factors explained 15.3% of the variance in deep learning( p < 0.05). Medical undergraduates generally demonstrated strong deep learning abilities. The challenge and curiosity factors of creative tendency emerged as the primary predictors of deep learning among medical students.

The authors' abstract, as published at the source. Scientific Reports, 2026 · DOI ↗