Keywords
AI addiction
behavioral addiction
psychometric validation
cognitive offloading
human–AI interaction
screening scale
students
psychological dependence
digital behavior
How to Cite
Abstract
The rapid integration of generative artificial intelligence (AI) into educational and everyday environments has intensified concerns regarding the emergence of new forms of behavioral addiction associated with excessive and maladaptive human–AI interaction. Despite the growing body of research in the field of human–AI interaction, contemporary psychometrics still lacks brief and validated instruments for screening the risk of generative AI addiction. The purpose of the present study was to develop and conduct a comprehensive psychometric validation of the AIAS-10+1 v4.0 (AI Addiction Scale), designed to assess socio-emotional and behavioral-cognitive manifestations of AI addiction.
The study involved 242 university students aged 18–30 years who were active users of generative AI technologies. Psychometric evaluation included assessment of internal consistency (Cronbach’s alpha), item-total correlations, exploratory factor analysis (EFA), correlation, regression and mediation analyses, ROC-analysis, Gaussian Mixture Modeling, K-Means cluster analysis, and threshold validation procedures. The initial 11-item version of the scale was revised according to the results of EFA and item analysis, resulting in the final AIAS-10+1 v4.0 model.
The final version of the instrument consists of 10 core items forming two factors: “Socio-Emotional Dependence” (α = 0.866) and “Behavioral-Cognitive Dependence” (α = 0.696), as well as a separate Critical Thinking Indicator (+1), evaluated independently from the general addiction index. The overall internal consistency of the scale was α = 0.795. The findings demonstrated that 20.2% of respondents belonged to the risk group for problematic AI use. Loneliness and stress were identified as significant psychological predictors of AI addiction, while mediation analysis confirmed that their influence is mediated through socio-emotional and cognitive-behavioral mechanisms of interaction with AI systems. ROC-analysis and Gaussian Mixture Modeling supported the validity of the proposed cutoff scores.
The obtained results indicate that AIAS-10+1 v4.0 is a reliable and psychometrically sound screening instrument for identifying the risk of addiction to generative artificial intelligence. The scale may be applied in psychological, educational, and interdisciplinary research, as well as in practical settings for early detection of maladaptive forms of AI-related behavior.
Keywords: generative artificial intelligence; AI addiction; behavioral addiction; psychometric validation; cognitive offloading; human–AI interaction; screening scale; students; psychological dependence; digital behavior.
Received: December 21, 2025
Accepted: March 20, 2026
Published: March 30, 2026
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Copyright (c) 2026 Andrii Chornomydz, Mariana Lukaniuk, Oleksandra Oleshchuk