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Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study

Author

Listed:
  • Xin Li

    (Department of Infectious Disease, The First Affiliated Hospital of China Medial University, Shenyang 110000, China)

  • Michael Yi-chao Jiang

    (Department of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong Kong, Hong Kong SAR, China
    Centre for Learning Sciences and Technologies, Hong Kong Institute of Educational Research, The Chinese University of Hong Kong, Hong Kong SAR, China)

  • Morris Siu-yung Jong

    (Department of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong Kong, Hong Kong SAR, China
    Centre for Learning Sciences and Technologies, Hong Kong Institute of Educational Research, The Chinese University of Hong Kong, Hong Kong SAR, China)

  • Xinping Zhang

    (Hunnan Tumour Centre, The First Affiliated Hospital of China Medical University, Shenyang 110000, China)

  • Ching-sing Chai

    (Department of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong Kong, Hong Kong SAR, China)

Abstract

Medical students learning to use artificial intelligence for medical practices is likely to enhance medical services. However, studies in this area have been lacking. The present study investigated medical students’ perceptions of and behavioral intentions toward learning artificial intelligence (AI) in clinical practice based on the theory of planned behavior (TPB). A sum of 274 Year-5 undergraduates and master’s and doctoral postgraduates participated in the online survey. Six constructs were measured, including (1) personal relevance (PR) of medical AI, (2) subjective norm (SN) related to learning medical AI, (3) perceived self-efficacy (PSE) of learning medical AI, (4) basic knowledge (BKn) of medical AI, (5) behavioral intention (BI) toward learning medical AI and (6) actual learning (AL) of medical AI. Confirmatory factor analysis and structural equation modelling were employed to analyze the data. The results showed that the proposed model had a good model fit and the theoretical hypotheses in relation to the TPB were mostly confirmed. Specifically, (a) BI had a significantly strong and positive impact on AL; (b) BI was significantly predicted by PR, SN and PSE, whilst BKn did not have a direct effect on BI; (c) PR was significantly and positively predicted by SN and PSE, but BKn failed to predict PR; (d) both SN and BKn had significant and positive impact on PSE, and BKn had a significantly positive effect on SN. Discussion was conducted regarding the proposed model, and new insights were provided for researchers and practitioners in medical education.

Suggested Citation

  • Xin Li & Michael Yi-chao Jiang & Morris Siu-yung Jong & Xinping Zhang & Ching-sing Chai, 2022. "Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:14:p:8733-:d:865277
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    References listed on IDEAS

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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Federico D’Antoni & Fabrizio Russo & Luca Ambrosio & Luca Bacco & Luca Vollero & Gianluca Vadalà & Mario Merone & Rocco Papalia & Vincenzo Denaro, 2022. "Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review," IJERPH, MDPI, vol. 19(10), pages 1-20, May.
    3. Jiyeon Lee & Sook Jung Kang, 2020. "Factors influencing nurses' intention to care for patients with emerging infectious diseases: Application of the theory of planned behavior," Nursing & Health Sciences, John Wiley & Sons, vol. 22(1), pages 82-90, March.
    4. Ching Sing Chai & Xingwei Wang & Chang Xu, 2020. "An Extended Theory of Planned Behavior for the Modelling of Chinese Secondary School Students’ Intention to Learn Artificial Intelligence," Mathematics, MDPI, vol. 8(11), pages 1-18, November.
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    1. Miao Yue & Morris Siu-Yung Jong & Yun Dai, 2022. "Pedagogical Design of K-12 Artificial Intelligence Education: A Systematic Review," Sustainability, MDPI, vol. 14(23), pages 1-29, November.

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