ArticleBMC medical education2026
Differential analysis of artificial intelligence teaching tools acceptance among medical teachers based on theory of planned behavior: a cross-sectional study with latent profile and multi-class logistic regression analysis.
Article in BMC medical 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
3 authors.
Funding
Abstract
backgroundThe integration of AI teaching tools into medical education presents transformative opportunities, yet the acceptance by medical teachers in resource-constrained regions of China remains pivotal for its adoption.
objectiveTo explore the latent profiles and influencing factors of AI teaching tools acceptance among medical teachers in these regions based on the TPB, and to provide evidence for formulating differentiated AI education promotion strategies in medical fields.
methodsA cross-sectional study was conducted among 220 medical teachers from 10 medical universities and affiliated hospitals in southwestern Sichuan and northwestern China between May and June 2025 using mixed non-probability sampling. Data were collected through a general information questionnaire, the TAAIS, and the GAAIS. LPA and multi-class logistic regression were employed to identify potential profiles and analyze influencing factors.
resultsThe optimal three-profile model identified distinct medical teacher acceptance profiles: Ambivalent-Hesitant (34.5%), Conservative-Skeptical (45.9%), and Proactive-Accepting (19.5%). Within the TPB framework, behavioral attitude showed the strongest association with profile membership; each one-point increase was associated with lower odds of belonging to the Ambivalent-Hesitant (OR = 0.535, P < 0.001) or Conservative-Skeptical (OR = 0.588, P < 0.001) than to the Proactive-Accepting. Subjective norms were more strongly associated with the Proactive-Accepting than with the Ambivalent-Hesitant. Perceived behavioral control was also associated with profile differentiation, reflected in significant self-efficacy differences across profiles (H = 116.451, P < 0.001) and in its association with behavioral intention. Teaching subject was additionally associated with acceptance heterogeneity (P < 0.050).
conclusionAcceptance of AI teaching tools among medical teachers in resource-constrained regions clustered into three latent profiles. Profile membership was associated with behavioral attitudes, subjective norms, and perceived behavioral control. Effective integration may therefore benefit from profile-specific strategies tailored to local resources, training capacity, and institutional culture. Longitudinal studies are warranted because our cross-sectional data cannot determine whether teachers transition between these profiles over time as experience with AI tools and institutional support accumulate. Such designs would help clarify adoption trajectories and the role of organizational conditions.
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.