To develop and examine the initial psychometric properties of the Academic AI Overreliance Scale among health sciences university students.
Approach:
Study 1: Conducted an Exploratory Factor Analysis (EFA) with 437 university students to identify the underlying structure of an initial 24-item pool.
Study 2: Performed a Confirmatory Factor Analysis (CFA) with an independent sample of 437 university students to evaluate the factorial validity, measurement invariance, and internal consistency of the refined 22-item version.
Key Findings:
Study 1 supported a two-factor solution: Emotional-Dysregulated Reliance and Cognitive Reliance, indicating distinct dimensions of overreliance.
Study 2 confirmed the correlated two-factor model with excellent fit indices (CFI = 0.994, TLI = 0.994, RMSEA = 0.066, SRMR = 0.056), suggesting strong model validity.
Internal consistency was adequate for both factors: Emotional-Dysregulated Reliance (α = 0.94, ω = 0.94) and Cognitive Reliance (α = 0.88, ω = 0.88), indicating reliable measurement.
Interpretation:
The findings indicate that Academic AI Overreliance may represent a specific form of cognitive and self-regulatory externalization associated with the integration of generative AI into academic activities.
Limitations:
The study was conducted in Ecuador, which may limit generalizability to other contexts and cultures.
The sample consisted solely of health sciences university students, which may affect the applicability of findings to other academic disciplines.
Conclusion:
The scale provides preliminary evidence of validity based on internal structure, measurement invariance, and reliability, and may serve as an initial instrument for future research on generative AI, academic reasoning, and intellectual autonomy in higher education.
