ArticleBMC medical education2026
From hype to reality: the feasibility, dilemmas, and solutions of Gen AI in medical education from students' perspectives.
Article in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- From Confidence to Commitment: Self-Perceived Competence and the Motivational Pathways to Intellectual Engagement with AI Learning Tools.Journal of Intelligence · 2026Article
- From Critical Thinking to Passive Acceptance: The Moderating Role of Decision Fatigue in Students' Use of Generative AI.European journal of investigation in health, psychology and education · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid evolution of generative artificial intelligence (AI) has sparked a pedagogical debate over whether AI can replace human teachers in medical education. What was once a theoretical inquiry has now become an urgent empirical question as AI technologies increasingly enter the classroom, challenging traditional notions of teaching, learning, and mentorship.
objectivesThis study aims to investigate the medical students' perceptions of generative AI as a potential replacement for traditional educators, focusing on the interrelationships among Feasibility, Dilemmas, Perception, and Replacement Intention. METHODOLOGY: Data were collected from 579 medical students using a structured questionnaire and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS. The measurement model demonstrated strong reliability and validity across all constructs.
resultsStructural analysis revealed that feasibility significantly influenced both perception (β = 0.295, p < 0.001) and replacement (β = 0.137, p < 0.001). At the same time, perception strongly predicted Replacement Intention (β = 0.314, p < 0.001) and mediated the feasibility-replacement relationship (β = 0.093, supported). However, Dilemmas did not moderate the feasibility-perception link (β = 0.045, p = 0.250), indicating that ethical or professional concerns had limited influence on students' acceptance of AI teaching. The Importance-Performance Map Analysis (IPMA) further identified perception as the most influential construct driving replacement intention.
conclusionThe findings, grounded in the Technology Acceptance Model (TAM) and Expectation-Confirmation Theory (ECT), suggest that medical students' acceptance of AI in education is shaped more by pragmatic feasibility and positive perception than by moral apprehension. The study concludes that while AI cannot yet replace the human teacher, its perceived feasibility and usefulness position it as a powerful complementary tool in reshaping the future of medical education.
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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.