Currently submitted to: JMIR Medical Education
Date Submitted: May 5, 2026
Open Peer Review Period: May 6, 2026 - Jul 1, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The Digital-Intelligent Learning Engagement Scale for Medical Students: Instrument Development and Psychometric Evaluation
ABSTRACT
Background:
The rapid integration of digital-intelligent technologies, including artificial intelligence and simulation, has reshaped medical education. While learning engagement is a critical indicator of educational quality, existing instruments fail to capture the unique aspects of engagement within these technology-rich contexts.
Objective:
This study aimed to develop and psychometrically evaluate the Digital-Intelligent Learning Engagement Scale for medical students, addressing the need for a comprehensive instrument to measure engagement in digitally transformed medical education environments.
Methods:
This was an instrument development and psychometric evaluation study conducted in two phases. Phase 1 involved scale development through theoretical modeling, qualitative interviews with 25 medical students, two-round Delphi expert consultation with eight experts, and pilot testing with 30 students. Phase 2 involved psychometric evaluation through a cross-sectional survey of 1499 medical students. The sample was randomly split for exploratory factor analysis (n=750) and confirmatory factor analysis (n=749). Construct validity, reliability, and item discrimination were assessed.
Results:
Exploratory factor analysis revealed a three-factor structure comprising Digital Deep Learning Efficacy (8 items), Digital Collaborative Learning (6 items), and Digital Self-Regulated Learning (6 items), explaining 66.13% of the total variance. Confirmatory factor analysis confirmed this structure with acceptable model fit (χ²/df=1.125, GFI=0.918, CFI=0.928, RMSEA=0.064). Content validity was established with I-CVI ranging from 0.85-1.00 and S-CVI of 0.94. Cronbach's alpha coefficients ranged from 0.781 to 0.865, with split-half reliability of 0.869. Item discrimination analysis demonstrated significant differences between upper and lower 27% groups (p<0.001).
Conclusions:
The DI-LES could be a valid and reliable instrument for measuring medical students' engagement in digital-intelligent learning environments. Its three-factor structure reflects the multidimensional nature of engagement in contemporary medical education, supporting its use for research and educational practice. The scale also offers practical applications for nursing education, particularly in assessing engagement with AI-assisted tools and virtual simulations.
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