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Yapay Zekâya Bağımlılık Ölçeğinin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması

Year 2024, Volume: 6 Issue: 3, 306 - 315, 30.09.2024
https://doi.org/10.56639/jsar.1509301

Abstract

Bu çalışmada, Morales-García ve ark. (2024) tarafından geliştirilmiş olan Yapay Zekâya Bağımlılık Ölçeğini (Scale for Dependence on Artificial Intelligence - DAI) Türkçe diline uyarlayarak güvenirlik ve geçerliliğinin incelenmesi amaçlanmıştır. Ölçek üniversite öğrencilerinin Yapay zekâya bağımlılık düzeylerini ölçmeyi amaçlamaktadır. Çalışma dört aşamada gerçekleştirilmiştir. Ölçeğin Türkçe’ye çevrilmesi, açımlayıcı ve doğrulayıcı faktör analizi, madde geçerliği, güvenirlik. Çalışma kapsamında 584 katılımcının oluşturduğu üniversite öğrencilerinden veri toplanmıştır. Ölçeğin geçerliliğini test etmek amacıyla Açımlayıcı Faktör Analizi ve Doğrulayıcı Faktör Analizi yapılmıştır. Açımlayıcı Faktör Analizinde ölçeğin tek boyutlu bir yapıda olduğu ve varyansın % 58,955’inin açıklandığı bulunmuştur. Güvenirlik için Cronbach Alfa iç tutarlılık katsayısı (.82) ve test- tekrar test değerleri (0,79) hesaplanmıştır. Tek boyut ve 5 maddeden oluşan Yapay Zekâya Bağımlılık Ölçeği için yapılan doğrulayıcı faktör analizinde; x2/df=2.609 [χ2=13.045 (Sd=, p<.05)], RMESA=.077, SRMR=.03, CFI=.98, GFI=.98, AGFI=.94, NFI=.97 ve TLI=.96 değerlerine ulaşılmıştır. Elde edilen uyum indeksleri değeri sonucunda modelin iyi bir uyuma sahip olduğu ortaya konulmuştur. Yapılan madde analizinde maddelerin ayırt edici özelliğe sahip olduğu görülmüştür. Buna göre, ölçeğin tamamı için elde edilen değerlerin ölçeğin güvenirliğine yönelik yeterli kanıtlar sunduğu söylenebilir. Türkçe diline uyarlanan Yapay Zekâya Bağımlılık Ölçeğinin, üniversite öğrencilerinin yapay zekâya bağımlılık düzeylerini ölçmek için geçerli ve güvenilir bir ölçme aracı olduğu sonucuna ulaşılmıştır.

References

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  • Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X. & Wang, Y. (2024). AI Technology panic-is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents. Psychology Research and Behavior Management, 1087-1102.
  • Huang, M. H. & Rust, R. T. (2018). Artificial intelligence in service, Journal of Service Research, 212, 155-172.
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  • Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351-354. https://doi.org/10.1016/j.chb.2013.10.059
  • Kaimara, P., Oikonomou, A., and Deliyannis, I. (2022). Could virtual reality applications pose real risks to children and adolescents? A systematic review of ethical issues and concerns. Virtual Real. 26, 697–735. https://doi.org/10.1007/s10055-021-00563-w
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Adaptation of the Dependence to Artificial Intelligence Scale into Turkish: Validity and Reliability Study

Year 2024, Volume: 6 Issue: 3, 306 - 315, 30.09.2024
https://doi.org/10.56639/jsar.1509301

Abstract

In this study, Morales-García et al. It was aimed to examine the reliability and validity of the Scale for Dependence on Artificial Intelligence (DAI) developed by (2024) by adapting it to the Turkish language. The scale aims to measure the addiction levels of university students to artificial intelligence. The study was carried out in four stages. Translation of the scale into Turkish, exploratory and confirmatory factor analysis, item validity, reliability. Within the scope of the study, data was collected from 584 participants, university students. Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted to test the validity of the scale. In the Exploratory Factor Analysis, it was found that the scale had a unidimensional structure and 58.955% of the variance was explained. For reliability, Cronbach Alpha internal consistency coefficient (.82) and test-retest values (0.79) were calculated. In the confirmatory factor analysis conducted for the Artificial Intelligence Addiction Scale, which consists of a single dimension and 5 items; x2/df=2.609 [χ2=13.045 (Sd=, p<.05)], RMESA=.077, SRMR= .03, CFI=.98, GFI=.98, AGFI=.94, NFI=.97 and TLI=.96. has been reached. As a result of the fit index values obtained, it was revealed that the model had a good fit. In the item analysis, it was seen that the items had distinctive features. Accordingly, it can be said that the values obtained for the entire scale provide sufficient evidence for the reliability of the scale. It has been concluded that the Artificial Intelligence Dependency Scale, adapted to the Turkish language, is a valid and reliable measurement tool to measure the addiction levels of university students to artificial intelligence.

References

  • Akıllı, H., Kemahlı, F., Okudan, K. & Polat, F. (2008). Ekolojik Ayak İzinin Kavramsal İçeriği ve Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi’nde Bireysel Ekolojik Ayak İzi Hesaplaması, Akdeniz Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(15), 21-35.
  • Akten, S., Gül, A. & Akten, M. (2012). Korunan Doğal Alanlarda Kullanılabilecek Ziyaretçi Yönetim Modelleri ve Karşılaştırılması. Turkish Journal of Forestry, 13(1), 57-65.
  • Andereck, K. L., Valentine, K. M., Knopf, R. C. & Vogt, C. A. (2005). Residents Perceptions of Community Tourism Impacts. Annals of Tourism Research. 32(4), 1056-1076.
  • Agarwal, R. (2022). Impact of Human Dependency on Artificial Intelligence, International Advanced Research Journal in Science, Engineering and Technology, 9 (9), 101-104.
  • Aharonovich, E., Liu, X., Nunes, E., and Hasin, D. S. (2002). Suicide attempts in substance abusers: effects of major depression in relation to substance use disorders. Am. J. Psychiatry 159, 1600–1602. https://doi.org/10.1176/appi.ajp.159.9.1600
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1-14.
  • Alpar, R. (2014). Spor Sağlık ve Eğitim Bilimlerinden Örneklerle Uygulamalı İstatistik ve Geçerlik Güvenirlik. 2. Baskı. Detay Yayıncılık. Ankara.
  • Aytaç, M. ve Öngen, B. (2012). Doğrulayıcı faktör analizi ile yeni çevresel paradigma ölçeğinin yapı geçerliliğinin incelenmesi, İstatistikçiler Dergisi, 5, 14-22.
  • Baumgartner, H. & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139-161.
  • Bayram, N. (2004). Sosyal bilimlerde SPSS ile veri analizi. Ezgi Kitabevi, Bursa.
  • Beard, K. W., & Wolf, E. M. (2001). Modification İn The Proposed Diagnostic Criteria For Internet Addiction. Cyberpsychology & Behavior, 4(3), 377-383.
  • Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186-3191.
  • Bu, E. T., & Skutle, A. (2013). After the ban of slot machines in Norway: A new group of treatment-seeking pathological gamblers? Journal of Gambling Studies, 29(1), 37-50. https://doi.org/10.1007/s10899-011-9287-4
  • Büyüköztürk, Ş. (2023). Sosyal Bilimler İçin Veri Analizi El Kitabı, İstatistik Araştırma Deseni SPSS Uygulamaları ve Yorum. 30. Baskı Pegem Akademi, Ankara.
  • Brown, S. A., Inaba, R. K., Gillin, J. C., Schuckit, M. A., Stewart, M. A., & Irwin, M. R. (1995). Alcoholism and affective disorder: Clinical course of depressive symptoms. Am. J. Psychiatry 152, 45-52. https://doi.org/10.1176/ajp.152.1.45
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford Press, New York.
  • Chianella, R. (2021). Addictive digital experiences: the influence of artificial intelligence and more-than-human design. In 14th International Conference of the European Academy of Design, Safe Harbours for Design Research, 9 (5), 1-13.
  • Dong, Y., Hou, J., Zhang, N., & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 1-10.
  • Dosovitsky, G. & Bunge, E. L. (2021). Bonding with bot: User feedback on a chatbot for social isolation. Frontiers in Digital Health, 3, 735053. https://doi.org/10.3389/fdgth.2021.735053
  • DSM. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Association, D. S. M. T. F., & American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5 (Vol. 5, No. 5). Washington, DC: American psychiatric association.
  • Evans, D. C. (2017). Bottlenecks: aligning UX design with user psychology. Apress.
  • Farghaly Abdelaliem, S. M., Dator, W. L. T., & Sankarapandian, C. (2023). The Relationship between Nursing Students’ Smart Devices Addiction and Their Perception of Artificial Intelligence. Healthcare, 11(1), 110.
  • Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Bus. Inf. Syst. Eng. 66, 111-126. https://doi.org/10.1007/s12599-023-00834-7
  • Gilder, D. A., Wall, T. L., & Ehlers, C. L. (2004). Comorbidity of select anxiety and affective disorders with alcohol dependence in Southwest California Indians. Alcohol. Clin. Exp. Res. 28, 1805-1813. https://doi.org/10.1097/01.ALC.0000148116.27875.B0
  • Gillath, O., Aİ, T., Branicky, M., Keshmiri, S., Davison, R. & Spaulding, R. (2021). Attachment and trust in artificial intelligence. Comput Human Behav. 115:106607. https://doi.org/10.1016/j.chb.2020.106607
  • Hair, J. F. Jr., Black, W. C., Babin, B. J. & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New Jersey: Pearson Prentice Hall.
  • Hambleton, R. K. & Patsula, L. (1999). Increasing the validity of adapted tests: Myths to be avoided and guidelines for improving test adaptation practices. Journal of Applied Testing Technology, 1(1), 1-30. Harrington, D. (2009). Confirmatory Factor Analysis. Oxford University Press. Oxford
  • Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling. A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Hu, B., Mao, Y. & Kim, K. J. (2023). How social anxiety leads to problematic use of conversational AI: The roles of loneliness, rumination, and mind perception. Comput Human Behav. 2023;143747. https://doi.org/10.1016/j.chb.2023.107760
  • Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X. & Wang, Y. (2024). AI Technology panic-is AI Dependence Bad for Mental Health? A Cross-Lagged Panel Model and the Mediating Roles of Motivations for AI Use Among Adolescents. Psychology Research and Behavior Management, 1087-1102.
  • Huang, M. H. & Rust, R. T. (2018). Artificial intelligence in service, Journal of Service Research, 212, 155-172.
  • Karasar, N. (2018). Bilimsel araştırma yöntemi. (24. Basım). Nobel Yayın Dağıtım Ankara
  • Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351-354. https://doi.org/10.1016/j.chb.2013.10.059
  • Kaimara, P., Oikonomou, A., and Deliyannis, I. (2022). Could virtual reality applications pose real risks to children and adolescents? A systematic review of ethical issues and concerns. Virtual Real. 26, 697–735. https://doi.org/10.1007/s10055-021-00563-w
  • Kaiser, H. F. (2016). The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151. https://doi.org/10.1177/001316446002000116
  • Kline, P. (2005). An Essay Guide to Factor Analysis. Routledge, London.
  • Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications, New York, NY.
  • Koo, T. K. & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163. https://doi.org/10.1016/j.jcm.2016.02.012
  • Krstić, L., Aleksić, V., & Krstić, M. (2022). Artificial intelligence in education: A review. Conference: 9th International Scientific Conference Technics and Informatics in Education, IT Education and Practice Review paper. 223-228. https://doi.org/10.46793/TIE22.223K
  • Leech, N., Barrett, K. C., & Borgan, G. A. (2005). SPSS for intermediate statistics use and intarpretation (Second Edition ed.). New Jersey, London.
  • Meyers, L. S., Gamst, G. & Guarino, A. J. (2006). Applied Multivariate Research: Design and Interpretation. Sage.
  • Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B. & Morales-García, M. (2024). Development and validation of a scale for dependence on artificial intelligence in university students. Front. Educ. 9:1323898. https://doi.org/10.3389/feduc.2024.1323898
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Details

Primary Language Turkish
Subjects Sport and Exercise Psychology
Journal Section Research Articles
Authors

Buğra Çağatay Savaş 0000-0002-8698-6311

Early Pub Date September 23, 2024
Publication Date September 30, 2024
Submission Date July 2, 2024
Acceptance Date August 28, 2024
Published in Issue Year 2024 Volume: 6 Issue: 3

Cite

APA Savaş, B. Ç. (2024). Yapay Zekâya Bağımlılık Ölçeğinin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması. Herkes için Spor Ve Rekreasyon Dergisi, 6(3), 306-315. https://doi.org/10.56639/jsar.1509301

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