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J Med Edu Dev 2026, 19(1): 56-65 |
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Al Harthi A, Al Ghafri T, Al-Zidjali L, Roychoudhury S. VARK learning preferences in preclinical medical students and their theoretical implications within the BLOOM-AI framework. , J Med Edu Dev. 2026; 19 (1) :56-65
URL: http://edujournal.zums.ac.ir/article-1-2586-en.html
URL: http://edujournal.zums.ac.ir/article-1-2586-en.html
1- College of Medicine and Health Sciences, Sultan Qaboos University
2- Directorate general of health services, Muscat, Ministry of health, Oman ,thamra74@yahoo.com
3- Oman College of Health Sciences
4- Department of Human and Clinical Anatomy, College of Medicine and Health Sciences, Sultan Qaboos University
2- Directorate general of health services, Muscat, Ministry of health, Oman ,
3- Oman College of Health Sciences
4- Department of Human and Clinical Anatomy, College of Medicine and Health Sciences, Sultan Qaboos University
Abstract: (1059 Views)
Background & Objective: Medical education is a complex, multidisciplinary field that can be stressful for medical students. This study aimed to investigate learning style preferences among pre-clinical medical students using the Visual, Auditory, Read/Write, Kinesthetic (VARK) assessment and to explore, through a theoretical mapping exercise, the implications for artificial intelligence-enhanced educational frameworks using the BLOOM-AI model.
Materials & Methods: A cross-sectional study was conducted among pre-clinical students (Phase I and II) at Sultan Qaboos University between October and December 2022. Participants completed an anonymous online questionnaire including demographics and the validated VARK questionnaire version 8.01. The VARK model classifies learning preferences into four distinct modalities: visual (V), aural (A), read/write (R), and kinesthetic (K). Statistical analysis employed chi-square tests. Findings were conceptually aligned with the BLOOM-AI pedagogical framework, yielding implications for the design of a potential AI-supported adaptive learning system.
Results: Of 179 respondents (65.9% female, mean age 20.0 ± 1.4 years), 120 students (67.0%) demonstrated multimodal learning preferences versus 59 (33.0%) with unimodal preferences. Kinesthetic learning was the most prevalent modality (24.0% as a unimodal preference and present in 78.2% of the overall sample). Among multimodal learners, 48 students (26.8%) exhibited quadmodal preferences, 33 (18.4%) trimodal preferences, and 39 (21.8%) bimodal preferences. Phase I students showed significantly higher quadmodal learning rates than Phase II students (51.0% vs 32.4%, p = 0.02). Theoretical mapping of the results within the BLOOM-AI framework revealed conceptual alignment between observed learning preferences and the framework’s design principles, which emphasize comprehensive sensory accommodation. This narrative analysis supports the use of AI tools in visual anatomical models, audio explanations, textual annotations, and kinesthetic simulations within integrated learning experiences. However, empirical validation of this framework's effectiveness remains necessary.
Conclusion: Multimodal learning preferences predominated among pre-clinical medical students, with kinesthetic modalities being highly prevalent. These findings could inform the implementation of AI-enhanced educational frameworks emphasizing comprehensive, multisensory learning support. Future research should evaluate AI-enhanced interventions designed in accordance with BLOOM-AI principles.
Materials & Methods: A cross-sectional study was conducted among pre-clinical students (Phase I and II) at Sultan Qaboos University between October and December 2022. Participants completed an anonymous online questionnaire including demographics and the validated VARK questionnaire version 8.01. The VARK model classifies learning preferences into four distinct modalities: visual (V), aural (A), read/write (R), and kinesthetic (K). Statistical analysis employed chi-square tests. Findings were conceptually aligned with the BLOOM-AI pedagogical framework, yielding implications for the design of a potential AI-supported adaptive learning system.
Results: Of 179 respondents (65.9% female, mean age 20.0 ± 1.4 years), 120 students (67.0%) demonstrated multimodal learning preferences versus 59 (33.0%) with unimodal preferences. Kinesthetic learning was the most prevalent modality (24.0% as a unimodal preference and present in 78.2% of the overall sample). Among multimodal learners, 48 students (26.8%) exhibited quadmodal preferences, 33 (18.4%) trimodal preferences, and 39 (21.8%) bimodal preferences. Phase I students showed significantly higher quadmodal learning rates than Phase II students (51.0% vs 32.4%, p = 0.02). Theoretical mapping of the results within the BLOOM-AI framework revealed conceptual alignment between observed learning preferences and the framework’s design principles, which emphasize comprehensive sensory accommodation. This narrative analysis supports the use of AI tools in visual anatomical models, audio explanations, textual annotations, and kinesthetic simulations within integrated learning experiences. However, empirical validation of this framework's effectiveness remains necessary.
Conclusion: Multimodal learning preferences predominated among pre-clinical medical students, with kinesthetic modalities being highly prevalent. These findings could inform the implementation of AI-enhanced educational frameworks emphasizing comprehensive, multisensory learning support. Future research should evaluate AI-enhanced interventions designed in accordance with BLOOM-AI principles.
Keywords: medical students, education, medical, undergraduate, learning preferences, learning style, artificial intelligence, educational technology, VARK
Editorial: Original Research |
Subject:
Medical Education
Received: 2025/09/11 | Accepted: 2025/12/2 | Published: 2026/01/1
Received: 2025/09/11 | Accepted: 2025/12/2 | Published: 2026/01/1
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