ArticleFrontiers in medicine2026
Generative AI and student learning performance in medical higher education: a social cognitive theory perspective.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Large Language Models as Simulated Candidates in Objective Structured Clinical Examinations: A Rubric-Mapping Proof-of-Concept Study.Advances in medical education and practice · 2026Article
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
Purpose: This study examines the impact of Generative Artificial Intelligence (GAI) on medical students' learning performance in higher education. Drawing on Social Cognitive Theory, it investigates the mediating roles of technology self-efficacy, perceived learning outcomes, and cognitive engagement, as well as the moderating role of fairness and ethical perceptions in AI-supported learning environments. Methods: A cross-sectional quantitative design was employed, with data collected from 326 medical higher education students in Pakistan using validated survey instruments. Structural equation modeling (SEM) with SmartPLS 4.0 was applied to test direct, mediating, and moderating relationships among the study variables. Results: The findings reveal that GAI-based technologies (GAT) exert a significant positive direct effect on students' learning performance and indirect effects through technology self-efficacy, perceived learning outcomes, and cognitive engagement. Technology self-efficacy and cognitive engagement emerged as strong mediators, while perceived learning outcomes further supported academic motivation and achievement. Moderation analysis demonstrated that perceptions of fairness and ethics significantly strengthen the positive effects of these mediators on learning performance, underscoring the importance of ethically grounded AI implementation. Conclusion: The study provides empirical evidence that GAI can enhance learning performance in medical higher education when supported by strong self-efficacy, engagement, and positive learning perceptions. Importantly, ethical and fair AI practices amplify these benefits. The findings offer practical implications for educators and policymakers seeking to integrate generative AI responsibly and effectively into medical education.
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.