Psychological mechanisms underlying medical students' continued use of DeepSeek: an explanatory sequential...

Generative AI is becoming widely used in medical education, but what drives medical students to continue using domestic tools such as DeepSeek after initial adoption remains poorly understood.

This study examined the psychological mechanisms underlying medical students continued use of DeepSeek, with particular attention to how cognitive appraisals, satisfaction, and task fit shaped continuance in real learning contexts.

An explanatory sequential mixed-methods design was used. In the quantitative phase, 630 valid questionnaires were analyzed using structural equation modeling to test a continuance pathway centered on cognitive appraisal, satisfaction, and behavioral intention, while interview data were used to explain unexpected and nonsignificant quantitative findings.

System quality and subjective norm positively affected perceived ease of use, while subjective norm and expectation confirmation positively affected perceived usefulness. Perceived ease of use and perceived usefulness both increased satisfaction, and satisfaction was the strongest predictor of continuance intention. Task-technology fit also positively influenced continuance intention, which strongly predicted actual continued use. Technology characteristics and task characteristics both improved task-technology fit. By contrast, information quality negatively affected perceived ease of use, subjective norm negatively affected satisfaction, and privacy concerns and expectation confirmation did not significantly affect continuance intention or satisfaction. Students mainly continued using DeepSeek because it was easy to access, helpful for academic writing and exam preparation, and suited to some specialized tasks; their primary concerns were unstable performance, inaccurate outputs, future pricing, and data security.

Continued DeepSeek use followed a cognitive-affective-behavioral sequence: perceived ease of use and usefulness drove satisfaction, which in turn predicted continuance intention (β = 0.769), while task-technology fit provided an independent behavioral pathway (β = 0.157), together accounting for actual continued use (β = 0.732).