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Background <p>The 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.</p> Objectives <p>This 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.</p> Methodology <p>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.</p> Results <p>Structural analysis revealed that feasibility significantly influenced both perception (β = 0.295, <i>p</i> &lt; 0.001) and replacement (β = 0.137, <i>p</i> &lt; 0.001). At the same time, perception strongly predicted Replacement Intention (β = 0.314, <i>p</i> &lt; 0.001) and mediated the feasibility-replacement relationship (β = 0.093, supported). However, Dilemmas did not moderate the feasibility-perception link (β = 0.045, <i>p</i> = 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.</p> Conclusion <p>The 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.</p>

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From hype to reality: the feasibility, dilemmas, and solutions of Gen AI in medical education from students’ perspectives

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月10日

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  • Uzma Sarwar,
  • Jameel Ahmed Bhutto,
  • Kiran Fazal,
  • Tong Sanhong,
  • Amir Mahmood,
  • Nadia Rehman

摘要

Background

The 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.

Objectives

This 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.

Results

Structural 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.

Conclusion

The 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.