Development and Initial Psychometric Assessment of a Scale Measuring Overreliance on Academic AI Among Health Sciences Students
By
Daniel Oleas
Jose A. Rodas
David Alarcón Rubio
July 21, 2026
Clinical Report: Development and Initial Psychometric Assessment of a Scale Measuring Overreliance on Academic AI Among Health Sciences Students
Overview This study developed the Academic AI Overreliance Scale and assessed its psychometric properties among health sciences students.
Background The integration of generative AI in higher education, particularly in health sciences, raises concerns about its impact on academic autonomy and cognitive skills. This study addresses a gap in the literature by providing a validated scale to measure overreliance on AI in academic settings.
Data Highlights Study Sample Size Factor Structure Fit Indices Internal Consistency Study 1 437 Two-factor solution N/A Emotional-Dysregulated Reliance: α = 0.94, ω = 0.94; Cognitive Reliance: α = 0.88, ω = 0.88 Study 2 437 Correlated two-factor model CFI = 0.994, TLI = 0.994, RMSEA = 0.066, SRMR = 0.056 N/A
Key Findings The Academic AI Overreliance Scale was developed with an initial 24-item pool, refined to 22 items. Study 1 identified a two-factor structure: Emotional-Dysregulated Reliance and Cognitive Reliance. Study 2 confirmed the two-factor model with excellent fit indices. Internal consistency was high for both factors. Clinical Implications Understanding these dynamics may inform future research on AI's role in academic reasoning and autonomy.
Conclusion The development of the Academic AI Overreliance Scale provides an instrument for examining the implications of AI integration in higher education.
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