Research Article

Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties

Volume: 10 May 6, 2026

Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties

Abstract

With the increasing prevalence of generative artificial intelligence use, addiction behaviors toward this technology have begun to attract the interest of researchers. Furthermore, addressing the multidimensional structure of generative artificial intelligence addiction through context-specific measurement tools can provide significant contributions for both researchers and practitioners. This study aims to adapt the multidimensional Generative Artificial Intelligence Dependency Scale (GAIDS) for a Turkish sample and examine its psychometric properties. The research sample consists of a total of 411 Turkish participants, comprising 341 women and 70 men. Confirmatory factor analysis validated the three-factor structure consistently with the original scale: Cognitive Preoccupation, Negative Consequences, and Withdrawal symptoms. The GAIDS exhibited a high level of internal consistency (α = 0.88; ω = 0.89). Correlation analyses conducted to examine criterion validity revealed significant positive relationships between the GAIDS and the Short Form of Young’s Internet Addiction Test and the Artificial Intelligence Chatbot Addiction Scale. These findings indicate that the Turkish adaptation of the GAIDS is a valid and reliable measurement tool for assessing generative artificial intelligence addiction.

Keywords

References

  1. Alagöz Hamzaj, Y. (2025). Generative AI acceptance among future educators: Personality and behavioral insights. Education and Information Technologies, 30, 23165–23188. https://doi.org/10.1007/s10639-025-13678-3
  2. Alharthi, S. A. (2025). Generative AI in game design: Enhancing creativity or constraining innovation? Journal of Intelligence, 13(6), 60. https://doi.org/10.3390/jintelligence13060060
  3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596
  4. Brand, M., & Potenza, M. N. (2021). How theoretical models can inspire advances in research and clinical practice: The example of behavioral addictions. Sucht, 67(4), 187-194. https://doi.org/10.1024/0939-5911/a000721
  5. Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognitive-Execution (I-PACE) model for Internet-use disorders: Update and clinical implications. Neuroscience & Biobehavioral Reviews, 104, 1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032
  6. Erdemir, N., & Atik, S. (2025). Validity and reliability analysis of the Artificial Intelligence-Digital Life Balance Scale. Psychiatric Quarterly. Advance online publication. https://doi.org/10.1007/s11126-025-10167-1
  7. Goh, A. Y. H., Hartanto, A., & Majeed, N. M. (2025). Generative artificial intelligence dependency: Scale development, validation, and its motivational, behavioral, and psychological correlates. Computers in Human Behavior Reports, 20, 100845. https://doi.org/10.1016/j.chbah.2025.100845
  8. Grant, J. E., Potenza, M. N., Weinstein, A., & Gorelick, D. A. (2010). Introduction to behavioral addictions. The American journal of drug and alcohol abuse, 36(5), 233-241. https://doi.org/10.3109/00952990.2010.491884

Details

Primary Language

English

Subjects

Psychological Counseling and Guidance (Other)

Journal Section

Research Article

Publication Date

May 6, 2026

Submission Date

February 11, 2026

Acceptance Date

April 17, 2026

Published in Issue

Year 2026 Volume: 10

APA
Seki, T., Şimşir Gökalp, Z., Abdilamitova, Z., Küçükdere, R. T., & Bayat, A. (2026). Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties. Research on Education and Psychology, 10. https://doi.org/10.54535/rep.1886848
AMA
1.Seki T, Şimşir Gökalp Z, Abdilamitova Z, Küçükdere RT, Bayat A. Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties. Research on Education and Psychology. 2026;10. doi:10.54535/rep.1886848
Chicago
Seki, Tolga, Zeynep Şimşir Gökalp, Zhumagul Abdilamitova, Rıza Taha Küçükdere, and Asiye Bayat. 2026. “Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties”. Research on Education and Psychology 10 (May). https://doi.org/10.54535/rep.1886848.
EndNote
Seki T, Şimşir Gökalp Z, Abdilamitova Z, Küçükdere RT, Bayat A (May 1, 2026) Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties. Research on Education and Psychology 10
IEEE
[1]T. Seki, Z. Şimşir Gökalp, Z. Abdilamitova, R. T. Küçükdere, and A. Bayat, “Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties”, Research on Education and Psychology, vol. 10, May 2026, doi: 10.54535/rep.1886848.
ISNAD
Seki, Tolga - Şimşir Gökalp, Zeynep - Abdilamitova, Zhumagul - Küçükdere, Rıza Taha - Bayat, Asiye. “Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties”. Research on Education and Psychology 10 (May 1, 2026). https://doi.org/10.54535/rep.1886848.
JAMA
1.Seki T, Şimşir Gökalp Z, Abdilamitova Z, Küçükdere RT, Bayat A. Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties. Research on Education and Psychology. 2026;10. doi:10.54535/rep.1886848.
MLA
Seki, Tolga, et al. “Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties”. Research on Education and Psychology, vol. 10, May 2026, doi:10.54535/rep.1886848.
Vancouver
1.Tolga Seki, Zeynep Şimşir Gökalp, Zhumagul Abdilamitova, Rıza Taha Küçükdere, Asiye Bayat. Turkish Version of the Generative AI Dependency Scale: Validity, Reliability, and Psychometric Properties. Research on Education and Psychology. 2026 May 1;10. doi:10.54535/rep.1886848

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