Measuring health professional students' willingness to use <scp>AI</scp> chatbots in learning: A fuzzy‐set qualitative comparative analysis

Abstract

Artificial intelligence (AI)-driven chatbots showed great potential in medical education. However, users' willingness to adopt them varied considerably, hindering their broader implementation. This study aimed to identify the combinations of factors influencing health professional students' use of AI chatbots in learning and to determine the pathways leading to a high willingness to adopt them. Fuzzy-set qualitative comparative analysis (fsQCA) was employed to examine configurations of factors influencing health professional students' high willingness to use AI chatbots, including performance expectancy, effort expectancy, and social influence. Four configurational pathways leading to high chatbot usage intention were identified: (1) Performance expectancy-Effort expectancy-Hedonistic motivation; (2) Performance expectancy-Facilitating conditions-Hedonistic motivation-Higher training levels; (3) Social influence-Facilitating conditions-Hedonistic motivation-Higher training levels; (4) Effort expectancy-Social influence-Hedonistic motivation-Lower training levels-female. The overall solution consistency was 0.94, with a coverage of 0.76, indicating that high usage intention was not attributable to a single factor but emerged from the joint effects of multiple conditions. Notably, hedonistic motivation emerged as a core condition across all four pathways, underscoring its central role in promoting chatbot usage intention. The findings suggested that intervention strategies should account for multiple pathways leading to the intention to use AI chatbots.

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