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Is generative artificial intelligence becoming addictive? An exploration among undergraduate students
Onur Isbulan1, Ozkan Ergene2, Eda Demirhan2
1Department of Instructional Technologies, Sakarya University, Sakarya, Türkiye.
Frontiers in Psychology
|August 12, 2026
Summary
Generative Artificial Intelligence (GAI) addiction is a multidimensional issue among students. Daily GAI use significantly predicts higher addiction scores across all measured dimensions.
Area of Science:
- Psychology
- Human-Computer Interaction
- Digital Well-being
Background:
- Generative Artificial Intelligence (GAI) use is increasing among students.
- The potential for GAI addiction necessitates empirical investigation.
- Existing addiction scales may not capture GAI-specific usage patterns.
Purpose of the Study:
- To develop and validate a psychometric scale for assessing Generative Artificial Intelligence Addiction (GAI-AS).
- To examine demographic and usage-related factors influencing GAI addiction tendencies in undergraduate students.
Main Methods:
- Development and validation of the Generative Artificial Intelligence Addiction Scale (GAI-AS) using factor analysis and reliability testing.
- Administration of the GAI-AS to a large sample of undergraduate students (N=919 for development, N=803 for validation).
- Statistical analyses including t-tests, ANOVAs, and correlation analyses to explore differences and relationships.
Main Results:
- The GAI-AS demonstrated strong psychometric properties (validity and reliability).
- No significant differences in GAI addiction were found based on gender or GAI version (free vs. paid).
- Daily GAI users exhibited significantly higher addiction scores across all dimensions compared to less frequent users.
Conclusions:
- GAI addiction is a valid psychological construct measurable by the GAI-AS.
- Frequency of GAI use is a critical factor associated with addiction tendencies.
- The findings provide a tool for understanding and addressing problematic GAI engagement.
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