ArticleAnatomical sciences education2026
Measuring health professional students' willingness to use AI chatbots in learning: A fuzzy-set qualitative comparative analysis.
Article in Anatomical sciences education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.