Adopting artificial intelligence driven technology in medical education
Abstract
Purpose: Artificial intelligence (AI) is a growing paradigm and has made considerable changes in many fields of study, including medical education. However, more investigations are needed to successfully adopt AI in medical education. The purpose of this study was identify the determinant factors in adopting AI-driven technology in medical education. Design/methodology/approach: This was a descriptive-analytical study in which 163 faculty members from Tabriz University of Medical Sciences were randomly selected by nonprobability sampling technique method. The faculty members’ intention concerning the adoption of AI was assessed by the conceptual path model of task-technology fit (TTF). Findings: According to the findings, “technology characteristics,” “task characteristics” and “TTF” showed direct and significant effects on AI adoption in medical education. Moreover, the results showed that the TTF was an appropriate model to explain faculty members’ intentions for adopting AI. The valid proposed model explained 37% of the variance in faulty members’ intentions to adopt AI. Practical implications: By presenting a conceptual model, the authors were able to examine faculty members’ intentions and identify the key determining factors in adopting AI in education. The model can help the authorities and policymakers facilitate the adoption of AI in medical education. The findings contribute to the design and implementation of AI-driven technology in education. Originality/value: The finding of this study should be considered when successful implementation of AI in education is in progress.
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Abdekhoda and Dehnad highlighted the challenge that arises when technology is misaligned with the tasks it is intended to support.
“Abdekhoda and Dehnad specifically highlighted the challenge that arises when technology is misaligned with the tasks it is intended to support and provided strategies to ensure successful technology adoption [180].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [180]
Challenges related to faculty members included lack of AI knowledge and expertise, resistance to adopting new technologies, and struggles with staff shortages.
“Challenges related to faculty members, such as the lack of AI knowledge and expertise [56, 180], resistance to adopting new technologies [5, 77, 180], and struggles with staff shortages, were also acknowledged in several studies [174].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [180]
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- Authors
- Abdekhoda, M., & Dehnad, A.
- Affiliations
- Tabriz University of Medical Sciences (IR)
- Iran University of Medical Sciences (IR)
- Corresponding author
- Abdekhoda, Mohammadhiwa
- Venue
- Interactive Technology and Smart Education
- Publisher
- Emerald
- Published
- 2024-01-29
- Volume
- 21
- Issue
- 4
- Pages
- 535-545
- ISSN
- 1741-5659 (ISSN-L)
- 1758-8510
- OpenAlex
- W4391225535
- Cited by
- 15
- References
- 26
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 1
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interactive Technology and Smart Education (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- artificial intelligence adoption, medical education, task-technology fit, technology characteristics, task characteristics, conceptual model
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