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Artificial Intelligence Literacy: An Adaptation Study

Year 2023, Volume: 4 Issue: 2, 291 - 306, 31.12.2023
https://doi.org/10.52911/itall.1401740

Abstract

The purpose of this research is to adapt the Artificial Intelligence Literacy Scale (AILS) developed by Wang et al. (2022) into Turkish and study its validity and reliability. The scale aims to measure the artificial intelligence literacy levels of non-expert adults. The research data were gathered from 402 participants, and the researchers did Confirmatory Factor Analysis (CFA) to test the validity of the adapted scale, and to test the reliability, they adopted Cronbach’s alpha technique. The adapted scale consists of 12 items and 4 factors, as is the case in the original version. CFA results indicate that X^2/df =1.82, RMSEA = 0.04, RMR = 0.03, NFI = 0.95, CFI = 0.98, GFI = 0.96 and AGFI = 0.94. Considering CFA results, it is concluded that the adapted scale is a good fit. As for reliability, as far as the factors are concerned, the internal consistency results are 0.72, 0.74, 0.76, and 0.72, respectively. Additionally, α=0.85 for the whole scale. Consideringly, the scale and its factors are adequately reliable, and the adapted scale can be used in Turkish culture.

References

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Yapay Zeka Okuryazarlığı: Bir Ölçek Uyarlama Çalışması

Year 2023, Volume: 4 Issue: 2, 291 - 306, 31.12.2023
https://doi.org/10.52911/itall.1401740

Abstract

Bu çalışmada Wang ve diğerleri (2022) tarafından geliştirilmiş Yapay Zeka Okuryazarlık Ölçeği’ni Türkçe diline uyarlayarak güvenirlik ve geçerliliğinin incelenmesi amaçlanmıştır. Ölçek yapay zeka konusunda uzman olmayan yetişkin bireylerin yapay zeka okuryazarlık düzeylerini ölçmeyi amaçlamaktadır. Çalışma kapsamında 402 katılımcının oluşturduğu yetişkin bireylerden veri toplanmıştır. Ölçeğin geçerliliğini test etmek amacıyla doğrulayıcı faktör analizi yapılmıştır. Güvenirliği için ise Cronbach Alpha iç tutarlılık katsayısı hesaplanmıştır. Dört boyut ve 12 maddeden oluşan Yapay Zeka Okuryazarlığı Ölçeği için yapılan doğrulayıcı faktör analizinde; x2/df için 1.82, RMSEA için 0.04, RMR için 0.03, NFI için 0.95, CFI için 0.98, GFI için 0.96 ve AGFI için 0.94 değerlerine ulaşılmıştır. Elde edilen uyum indeksleri değeri sonucunda modelin iyi bir uyuma sahip olduğu ortaya konulmuştur. Güvenlik analizi için yapılan Cronbach’s Alpha iç tutarlılık katsayısının hesaplanmasında ölçeğin alt boyutları için sırasıyla 0.72, 0.74, 0.76, 0.72 değerlerine ulaşılmıştır. Ölçeğin tümü için 0.85 iç tutarlılık katsayısı hesaplanmıştır. Buna göre ölçeğin hem boyutları hem de tamamı için elde edilen değerler ölçeğin güvenirliğine yönelik yeterli kanıtlar sunmaktadır. Türkçe diline uyarlanan Yapay Zeka Okuryazarlık Ölçeği’nin, yapay zeka konusunda uzman olmayan yetişkin bireylerin yapay zeka okuryazarlık düzeylerini ölçmek için geçerli ve güvenilir bir ölçme aracı olduğu sonucuna ulaşılmıştır.

References

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  • Calvani, A., Fini, A., & Ranieri, M. (2009). Assessing digital competence in secondary education. Issues, models and instruments. M. Leaning içinde, Issues in information and media literacy: Education, practice and pedagogy (s. 153-172). Informing Science Press.
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  • Cihangir Çankaya, Z. (2009). Autonomy support, basic psychological need satisfaction and subjective well-being: Self-determination theory. Turkish Psychological Counseling and Guidance Journal, 4(31), 23-31.
  • Çelebi, C., Demir, U., & Karakuş, F. (2023). Artificial intelligence literacy: A systematic review. Journal of Necmettin Erbakan University Ereğli Faculty of Education, 5(2), 536-560. https://doi.org/10.51119/ereegf.2023.67
  • Eshet, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of educational multimedia and hypermedia, 13(1), 93-106.
  • Ferrari, A. (2012). Digital competence in practice: An analysis of frameworks. Publications Office of the European Union.
  • Gapski, H. (2007). Some reflections on digital literacy. Proceedings of the 3rd International Workshop on Digital Literacy (s. 49-55). Crete, Greece: CEUR Workshop Proceedings. Retrieved from http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-310/paper05.pdf
  • Elrehail, H. (2018). The relationship among leadership, innovation and knowledge sharing: A guidance for analysis. Data in Brief, 19, 128–133. https://doi.org/10.1016/j.dib.2018.04.138
  • Ferikoğlu, D., & Akgün, E. (2022). An ınvestigation of teachers’ artificial intelligence awareness: A scale development study. Malaysian Online Journal of Educational Technology, 10(3), 215–231. https://doi.org/10.52380/mojet.2022.10.3.407
  • Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.
  • Gerbing, D. W., & Hamilton, J. G. (1996). Viability of exploratory factor analysis as a precursor to confirmatory factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 3(1), 62–72. https://doi.org/10.1080/10705519609540030
  • Gillaspy, J. A. Jr. (1996). A primer on confirmatory factor analysis. The Annual Meeting of the Southwest Educational Research Association.
  • Goretzko, D., Siemund, K., & Sterner, P. (2023). Evaluating model fit of measurement models in confirmatory factor analysis. Educational and Psychological Measurement, https://doi.org/10.1177/00131644231163813
  • Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14, 1191628. https://doi.org/10.3389/fpsyg.2023.1191628
  • Gür, Y. E., Ayden, C., & Yücel, A. (2019). Effects to human resources managements of developments in artificial intelligence. International Journal of Economics and Administrative Sciences, 3(2), 137-158.
  • Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. https://doi.org/10.1177/0008125619864925
  • Hox, J. (2021). Confirmatory factor analysis. J. Barnes, & D. R. Forde içinde, The Encyclopedia of Research Methods in Criminology and Criminal Justice. John Wiley & Sons, Inc.
  • Hu, L., & 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
  • Hurley, A. E., Scandura, T. A., Schriesheim, C. A., Brannick, M. T., Seers, A., Vandenberg, R. J., & Williams, L. J. (1997). Exploratory and confirmatory factor analysis: guidelines, issues, and alternatives. Journal of Organizational Behavior, 18(6), 667–683. https://doi.org/10.1002/(SICI)1099-1379(199711)18:6<667::AID-JOB874>3.0.CO;2-T
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  • Jöreskog, K. G. & Sörbom, D. (2019). LISREL 8.80. Mooresville, Ind. : Scientific Software,
  • Karpunina, E. K., Dedov, S. V., Kholod, M. V., Ponomarev, S. V., & Gorlova, E. A. (2020). Artificial intelligence and its impact on economic security: trends, estimates and forecasts. E. G. Popkova, & B. S. Sergi içinde, Scientific and Technical Revolution: Yesterday, Today and Tomorrow (p. 213-225). Springer. https://doi.org/10.1007/978-3-030-47945-9_23
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  • Karagöz, B., & İrge, N. T. (2023). The effect of personnel empowerment on organizational commitment and individual work performance: A research in the health sector. Eurasian Academy of Sciences Social Sciences Journal, (48), 46-53.
  • Kaya, F., Aydın, F., Schepman, A., Rodway, P., Yetişensoy, O., & Kaya, M. D. (2022). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction. https://doi.org/10.1080/10447318.2022.2151730
  • Kieslich, K., Lünich, M. & Marcinkowski, F. The Threats of Artificial Intelligence Scale (TAI). Int J of Soc Robotics 13, 1563–1577 (2021). https://doi.org/10.1007/s12369-020-00734-w
  • Kim, S. W., & Lee, Y. (2022). The Artificial Intelligence Literacy Scale for Middle School Students., 27(3), 225-238. https://doi.org/10.9708/jksci.2022.27.03.225
  • Kurudayıcıoğlu, M., & Tüzel, S. (2010). The types of literacy of the 21st century, changing text comprehension and Turkish teaching. Türklük Bilimi Araştırmaları, 28, 281-298.
  • Laupichler, M. C., Aster, A., & Raupach, T. (2023a). Delphi study for the development and preliminary validation of an item set for the assessment of non-experts' AI literacy. Computers and Education: Artificial Intelligence, 4. https://doi.org/10.1016/j.caeai.2023.100126
  • Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023b). Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis. Computers in Human Behavior Reports, 12. https://doi.org/10.1016/j.chbr.2023.100338
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Details

Primary Language English
Subjects Development of Science, Technology and Engineering Education and Programs, Other Fields of Education (Other)
Journal Section Research Articles
Authors

Celalettin Çelebi 0000-0002-2189-6403

Fatih Yılmaz 0000-0001-7852-6756

Uğur Demir 0000-0002-1774-0369

Ferhat Karakuş 0000-0001-5327-8346

Early Pub Date December 31, 2023
Publication Date December 31, 2023
Submission Date December 7, 2023
Acceptance Date December 31, 2023
Published in Issue Year 2023 Volume: 4 Issue: 2

Cite

APA Çelebi, C., Yılmaz, F., Demir, U., Karakuş, F. (2023). Artificial Intelligence Literacy: An Adaptation Study. Instructional Technology and Lifelong Learning, 4(2), 291-306. https://doi.org/10.52911/itall.1401740

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