Psychometric Properties of an Artificial Intelligence Addiction Scale (AIAS) in University Students
DOI:
https://doi.org/10.69653/iah20231Keywords:
Artificial Intelligence, Addiction, Psychometric Properties, University Students, Scale ValidationAbstract
Background: Addiction is a multifaceted disorder that has evolved to include behaviors related to excessive use of digital technologies. In the university context, dependence on artificial intelligence (AI) systems raises significant concerns about its impact on students' mental health and academic performance. While addiction to specific technologies, such as smartphones, has been studied, addiction to AI is an emerging phenomenon that requires proper and contextualized assessment. Objective: The objective of this study was to adapt and evaluate the psychometric properties of an artificial intelligence addiction scale in university students. Methods: An instrumental study was conducted with a sample of 275 university students aged between 18 and 45 years (M = 20.51, SD = 4.20). The adaptation of the Online Gaming Addiction Questionnaire to the context of AI followed a rigorous process of translation and cultural validation. A confirmatory factor analysis (CFA) was conducted to evaluate the unidimensional structure of the scale, along with reliability analyses and measurement invariance tests by gender. Results: The CFA indicated an adequate fit for the one-factor model: χ² = 84.130, df = 35, CFI = 0.96, TLI = 0.94, RMSEA = 0.07, SRMR = 0.04. Reliability coefficients were high, with a Cronbach's alpha of 0.94 and a McDonald's omega of 0.93. Measurement invariance by gender was confirmed through hierarchical models, with differences in CFI less than 0.010, indicating that the scale measures consistently across males and females. Conclusions: The adaptation and evaluation of the AI Addiction Scale (AIAS) demonstrate that this tool is valid and reliable for measuring AI addiction in university students. The findings suggest that the AIAS can be effectively used in future research and in the development of interventions to address this emerging addictive behavior, contributing to the understanding and management of technological addiction in educational contexts.
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