Development and Validation of the AI Addiction Scale (AIAS-10): Measuring Compulsive and Emotional Dependence on AI Tools Among EFL Learners
DOI:
https://doi.org/10.18326/register.v19i1.1-27Keywords:
AI addiction, behavioral measurement, scale validation, digital dependency, compulsive technology use.Abstract
The current study developed and validated the AI Addiction Scale (AIAS-10) to measure problematic use of AI tools, such as ChatGPT. The purpose of the study was to address the lack of standardized instruments for assessing AI-related behavioral dependency in educational settings. Participants consisted of Muslim ELT/ELL students, reflecting the growing integration of AI tools in language learning contexts. Data from 267 users (students and professionals) were analysed to examine the psychometric properties of the scale. Factor analyses were conducted to identify its underlying structure, and reliability analyses were performed to assess internal consistency. The results showed that the scale reliably identifies two key dimensions of AI addiction: compulsive overuse and emotional dependence. The findings suggest that young adult learners in Muslim educational contexts may be more susceptible to over-reliance on AI for cognitive and language-related tasks. The AIAS-10 effectively captures unique AI-related issues, including over-reliance on cognitive tasks and anthropomorphizing AI systems. As AI becomes increasingly embedded in daily life, this validated tool provides researchers and clinicians with an important method for identifying unhealthy usage patterns. The findings highlight the need for educational guidelines and digital wellbeing strategies that promote balanced AI use in language learning, particularly within Muslim learner contexts.
References
Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012).
Development of a Facebook Addiction Scale. Psychological Reports, 110(2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517
Billieux, J., Maurage, P., Lopez-Fernandez, O., Kuss, D. J., & Griffiths, M. D. (2015).
Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Current Addiction Reports, 2(2), 156–162. https://doi.org/10.1007/s40429-015-0054-y
Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. (2016).
Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person–Affect–Cognition–Execution (I-PACE) model. Neuroscience & Biobehavioral Reviews, 71, 252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033
Browne, M. W., & Cudeck, R. (1993).
Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Sage.
Cohen, J. (1988).
Statistical power analysis for the behavioral sciences (2nd ed.). Routledge Academic.
DeVellis, R. F. (2017).
Scale development: Theory and applications (4th ed.). Sage Publications.
Field, A. (2018).
Discovering statistics using IBM SPSS statistics (5th ed.). Sage.
Griffiths, M. D. (2005).
A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010).
Multivariate data analysis (7th ed.). Pearson.
Hinkin, T. R. (1998).
A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods, 1(1), 104–121. https://doi.org/10.1177/109442819800100106
Hu, L. T., & Bentler, P. M. (1999).
Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Kline, R. B. (2016).
Principles and practice of structural equation modeling (4th ed.). The Guilford Press.
Kuss, D. J., & Griffiths, M. D. (2015).
Social networking sites and addiction: Ten lessons learned. International Journal of Environmental Research and Public Health, 12(3), 1280–1306. https://doi.org/10.3390/ijerph120301280
Kwon, M., Kim, D. J., Cho, H., & Yang, S. (2013).
The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE, 8(12), e83558. https://doi.org/10.1371/journal.pone.0083558
Nunnally, J. C., & Bernstein, I. H. (1994).
Psychometric theory (3rd ed.). McGraw-Hill.
Rani, S., Kumar, S., & Rani, A. (2023).
Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations. International Journal of Educational Technology in Higher Education, 20, Article 15. https://doi.org/10.1186/s41239-024-00467-0
Smith, J. A., & Doe, A. B. (2024).
Development and validation of a scale for dependence on artificial intelligence in university students. Frontiers in Education, 9, Article 1323898. https://doi.org/10.3389/feduc.2024.1323898
Tabachnick, B. G., & Fidell, L. S. (2019).
Using multivariate statistics (7th ed.). Pearson Education.
Young, K. S. (1998).
Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior, 1(3), 237–244. https://doi.org/10.1089/cpb.1998.1.237
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Süleyman Kasap , Mehmet Veysi Babayiğit

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
COPYRIGHT NOTICE
REGISTER JOURNAL: https://ejournal.uinsalatiga.ac.id/index.php/register/index is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
Copyright Notice
An author who publishes in REGISTER JOURNAL agrees to the following terms:
- The author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- The author can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgment of its initial publication in this journal.
- The author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-ShareAlike 4.0 Licence here: https://creativecommons.org/licenses/by-sa/4.0/.
Privacy Statement
The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal. They will not be made available for any other purpose or to any other party.





