Turkish Adaptation of the ChatGPT Literacy Scale

Öz

This study aims to adapt the ChatGPT Literacy Scale developed by Lee and Park (2024) into Turkish and examine its validity and reliability. The scale evaluates ChatGPT literacy across five dimensions: technical competence, critical evaluation, communication competence, creative application, and ethical competence. The adaptation followed a rigorous procedure to ensure linguistic and conceptual equivalence, including expert reviews and back-translation. Initially piloted with a small group, the scale was revised based on feedback and later tested on a broader sample of aviation students (n=143 in the first stage; n=462 in the second). Statistical analyses confirmed the preservation of the original structure with five dimensions and 25 items. The Turkish version proved to be a valid and reliable measurement tool. The findings suggest that the adapted scale can effectively assess ChatGPT literacy among aviation students, contributing to the integration of artificial intelligence technologies into educational and professional development settings.

Anahtar Kelimeler

• Aviation
• ChatGPT
• Digital Literacy
• Artificial Intelligence Literacy

ChatGPT Okuryazarlığı Ölçeği'nin Türkçeye Uyarlanması

Öz

Bu çalışma, Lee ve Park (2024) tarafından geliştirilen ChatGPT Okuryazarlığı Ölçeği’nin Türkçeye uyarlanmasını amaçlamaktadır. ChatGPT okuryazarlığını teknik yeterlilik, eleştirel değerlendirme, iletişim yeterliliği, yaratıcı uygulama ve etik yeterlilik olmak üzere beş temel boyutta ele alan ölçek, dilsel ve kavramsal uyumun sağlanması için uyarlanmıştır. Ölçeğin çeviri sürecinde dil uzmanları ve alan uzmanları tarafından gerçekleştirilen çeviriler arasındaki uyum kontrol edilmiş orijinal metin ile Türkçe versiyon arasındaki anlam bütünlüğü sağlanmıştır. İlk aşamada pilot bir grup üzerinde uygulanan ölçek, geri bildirimler doğrultusunda düzenlenmiş ve nihai hali daha geniş bir örneklem üzerinde test edilmiştir. Çalışmanın örneklemi havacılık öğrencilerinden oluşmuş, ilk aşamada 143, ikinci aşamada ise 462 öğrenciden veri toplanmıştır. İstatistiksel analizler sonucunda ölçeğin beş boyut ve 25 maddeden oluşan yapısının korunarak Türkçe formunun geçerli ve güvenilir bir ölçüm aracı olduğu belirlenmiştir. Araştırma, ChatGPT Okuryazarlığı Ölçeği’nin havacılık bağlamında etkin bir değerlendirme aracı olarak kullanılabileceğini ortaya koyarak yapay zekâ teknolojilerinin eğitim ve mesleki gelişim süreçlerinde kullanımına katkı sağlamaktadır.

Anahtar Kelimeler

• Havacılık
• ChatGPT
• Dijital Okuryazarlık
• Yapay Zekâ Okuryazarlığı

Kaynakça

1. Abubakar, M., EriOluwa, O., Teyei, M., & Al-Turjman, F. (2022, October). AI Application in the Aviation Sector. In 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs) (pp. 52-55). IEEE.
2. Al-Abdullatif, A. M., & Alsubaie, M. A. (2024). ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy. Behavioral sciences, 14(9), 845. https://doi.org/10.3390/bs14090845
3. Alkalbani, M. (2023). ChatGPT Technology and Its Role in Promoting Creativity in Education. The Asian Conference on Education 2023 Official Conference Proceedings, 1-19. https://papers.iafor.org/wp-content/uploads/papers/ace2023/ACE2023_73951.pdf
4. Altıntop, M. (2023). Yapay Zekâ/Akıllı Öğrenme Teknolojileriyle Akademik Metin Yazma: Chatgpt Örneği. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (46), 186-211.
5. An, J., Ding, W., & Lin, C. (2023). ChatGPT. tackle the growing carbon footprint of generative AI, 615, 586. https://www.ltimindtree.com/wp-content/uploads/2023/02/ChatGPT-An-AI-NLP-Model-POV.pdf
6. Aşkun, V. (2023). Sosyal Bilimler Araştırmaları İçin Chatgpt Potansiyelinin Açığa Çıkarılması: Uygulamalar, Zorluklar Ve Gelecek Yönelimler. Erciyes Akademi, 37(2), 622-656. https://doi.org/10.48070/erciyesakademi.1281544
7. Ballı, Ö. (2020). Yapay zekâ ve sanat uygulamaları üzerine güncel bir değerlendirme. Sanat ve Tasarım Dergisi, (26), 277-306.
8. Bernabeo, A., Goundar, S., Nguyen, K. V., Thien, B. N., Luong, Q., & Dinh, M. N. (2023). Artificial Intelligence for Safety Related Aviation Systems: A Roadmap in the Context of Vietnam. In Information Systems Research in Vietnam, Volume 2: A Shared Vision and New Frontiers (pp. 37-52). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-4792-8_4

9. Bhargava, R., & D’Ignazio, C. (2015, April). Designing tools and activities for data literacy learners. In Workshop on data literacy, Webscience.
10. Binns, R. (2018, January). Fairness in machine learning: Lessons from political philosophy. In Conference on fairness, accountability and transparency (pp. 149-159). PMLR.
11. Bozkurt, A. (2023). ChatGPT, üretken yapay zeka ve algoritmik paradigma değişikliği. Alanyazın, 4(1), 63-72. https://doi.org/10.59320/alanyazin.1283282
12. Büyüköztürk, Ş. (2011). Sosyal Bilimler için Veri Analizi El Kitabı. Ankara: Pegem Akademi. Sayfa 171.
13. Çankaya, D. (2020). Havacılıkta Yaygınlaşan Yapay Zekâ, API ve Büyük Veri Temelli Çözümler. Academic Perspective Procedia, 3(1), 465-473. https://doi.org/10.33793/acperpro.03.01.93
14. Demir, G., Moslem, S., & Duleba, S. (2024). Artificial Intelligence in Aviation Safety: Systematic Review and Biometric Analysis. International Journal of Computational Intelligence Systems, 17(1), 1-30. https://doi.org/10.1007/s44196-024-00671-w
15. DiSessa, A. A. (2018). Computational literacy and “the big picture” concerning computers in mathematics education. Mathematical thinking and learning, 20(1), 3-31. https://doi.org/10.1080/10986065.2018.1403544
16. Dobson, T., & Willinsky, J. (2009). Digital literacy. The Cambridge handbook of literacy, 10, 286-312.
17. Ehsan, U., & Riedl, M. O. (2020). Human-centered explainable ai: Towards a reflective sociotechnical approach. In HCI International 2020-Late Breaking Papers: Multimodality and Intelligence: 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings 22 (pp. 449-466). Springer International Publishing. https://doi.org/10.1007/978-3-030-60117-1_33
18. Eren, Z. (2021). Eğitimde yapay zeka uygulamaları ve geleceğe ilişkin yönelimler. (Ed. N. Öykü İyigün ve Mustafa K. Yılmaz). Yapay zeka: Güncel Yaklaşımlar ve Uygulamalar. İstanbul: Beta Kitap Yayıncılık. s.187-212.
19. Field, A. (2009). Discovering Statistics Using SPSS. London: Sage.
20. Freire, P. (1970). The adult literacy process as cultural action for freedom. Harvard educational review, 40(2), 205-225. https://doi.org/10.17763/haer.40.2.q7n227021n148p26
21. Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on information systems (TOIS), 14(3), 330-347. https://doi.org/10.1145/230538.230561
22. Garcia, A. B., Babiceanu, R. F., & Seker, R. (2021, April). Artificial intelligence and machine learning approaches for aviation cybersecurity: An overview. In 2021 integrated communications navigation and surveillance conference (ICNS) (pp. 1-8). IEEE. https://doi.org/10.1109/ICNS52807.2021.9441594.
23. Güneş, F. (2019). Okuryazarlık yaklaşımları. The Journal of Limitless Education and Research, 4(3), 224-246. https://doi.org/10.29250/sead.634908
24. Hassan, K., Thakur, A. K., Singh, G., Singh, J., Gupta, L. R., & Singh, R. (2024). Application of artificial intelligence in aerospace engineering and its future directions: A systematic quantitative literature review. Archives of Computational Methods in Engineering, 31(7), 4031-4086. https://doi.org/10.1007/s11831-024-10105-7
25. Hava, K., & Babayiğit, Ö. (2024). Exploring the relationship between teachers’ competencies in AI-TPACK and digital proficiency. Education and Information Technologies, 1-18. https://doi.org/10.1007/s10639-024-12939-x
26. Hepgüven, B. (2024, Eylül 30). ChatGPT ve Akademik Metinlerde İntihal İncelemesi. Hukuk ve Bilişim 3. Nesil Hukuk Dergisi. https://www.hukukvebilisimdergisi.com/chatgpt-ve-akademik-metinlerde-intihal-incelemesi/?utm_source=chatgpt.com
27. Holstein, K., Aleven, V., & Rummel, N. (2020). A conceptual framework for human–AI hybrid adaptivity in education. In Artificial Intelligence in Education: 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I 21 (pp. 240-254). Springer International Publishing. https://doi.org/10.1007/978-3-030-52237-7_20
28. Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit. The Electronic Journal of Business Research Methods, 6, 53-60.
29. 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
30. İmamoğlu, S. Z., Erat, S., & İnce, H. (2023). Yönetim Biliminde Yapay Zekâ. 1.bs., Nobel.
31. Kaiser, M. O. (1974). Kaiser-Meyer-Olkin measure for identity correlation matrix. Journal of the Royal Statistical Society, 52(1), 296-298.
32. Kanki, B. G., Anca, J., & Chidester, T. R. (Eds.). (2019). Crew resource management (3rd ed.). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-812995-1.00004-X
33. Karakoç, A. G. D. F. Y., & Dönmez, L. (2014). Ölçek geliştirme çalışmalarında temel ilkeler. Tıp Eğitimi Dünyası, 13(40), 39-49. https://doi.org/10.25282/ted.228738
34. Karasar, N. (2010). Bilimsel Araştırma Yöntemi. Ankara: Nobel Yayın Dağıtım.
35. Kline, R. B. (2023). Principles and practice of structural equation modeling. New York: Guilford
36. Koç, M., & Barut, E. (2016). Development and validation of New Media Literacy Scale (NMLS) for university students. Computers in human behavior, 63, 834-843. https://doi.org/10.1016/j.chb.2016.06.035
37. Laupichler, M. C., Aster, A., & Raupach, T. (2023). 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, 100126. https://doi.org/10.1016/j.caeai.2023.100126
38. Lee, S., & Park, G. (2024). Development and validation of ChatGPT literacy scale. Current Psychology, 1-13. https://doi.org/10.1007/s12144-024-05723-0
39. Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1-16). https://doi.org/10.1145/3313831.3376727
40. Madunić, J., & Sovulj, M. (2024). Application of ChatGPT in Information Literacy Instructional Design. Publications, 12(2), 11. https://doi.org/10.3390/publications12020011
41. Meydan, C. H. (2023). Havayolu işletmelerinde dijital dönüşüm uygulamaları üzerine bir inceleme. Journal of Aviation Research, 5(1), 65-82. https://doi.org/10.51785/jar.1185935
42. Millar, R. (2006). Twenty first century science: Insights from the design and implementation of a scientific literacy approach in school science. International journal of science education, 28(13), 1499-1521. https://doi.org/10.1080/09500690600718344
43. Nilsson, N. J. (1998). Artificial intelligence: a new synthesis. Morgan Kaufmann.
44. Pallant, J. (2001). SPSS survival manual. UA: Maidenhead.
45. Prudon, P. (2014). Confirmatory factor analysis: A brief introduction and critique. Qualtrics, P. UT, USA, 10(1.476).
46. Psailla, G. & Roland, W. (2007). E-commerce and web technologies: 8th International Conference, EC-Web 2007, Regensburg, Germany, September 3-7, Proceedings, 4655, Springer, 2007.
47. Rahman, A. (2023) AI Revolution: Shaping Industries through Artificial Intelligence and Machine Learning. Journal Environmental Sciences and Technology, 2, 93-105.
48. Sakib, M. S. I. (2023). What is ChatGPT. ResearchGate.
49. Schank, R. C. (1984). The cognitive computer: On language, learning, and artificial intelligence. Addison-Wesley Longman Publishing Co., Inc..
50. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of psychological research online, 8(2), 23-74.
51. Seçer, İ. (2018). Psikolojik test geliştirme ve uyarlama süreci: SPSS ve LISREL uygulamaları. Ankara: Anı yayıncılık.
52. Shah, M., & Sureja, N. (2024). A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions. Archives of Computational Methods in Engineering, 1-13. https://doi.org/10.1007/s11831-024-10134-2
53. Şahin, Y. (2024). Havacılık ve Yapay Zekâ Alanındaki Çalışmaların Bibliyometrik Analizi. International Journal of Social and Humanities Sciences Research (JSHSR), 11(110), 1637-1648. https://doi.org/10.5281/zenodo.13732160
54. Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics. Pearson.
55. Tabachnick, B.G. and Fidell, L.S. (2001). Using Multivariate Statistics. Boston: Allyn and Bacon
56. Tay, L., & Jebb, A. (2017). Scale Development. In S. Rogelberg (Ed), The SAGE Encyclopedia of Industrial and Organizational Psychology. CA: Sage.
57. TheVerge (2024, December 4). ChatGPT now has over 300 million weekly users. https://www.theverge.com/2024/12/4/24313097/chatgpt-300-million-weekly-users?utm_source=chatgpt.com
58. UNF (t.y.). Evaluating ChatGPT-Generated Content. https://unf.pressbooks.pub/chatgptinhighereducation/chapter/evaluating-chatgpt-generated-content/?utm_source=chatgpt.com
59. Uslu, B. (2023). Üniversitelerde Yapay Zekanın Kullanım Alanları: Potansiyel Yararları ve Olası Zorluklar. Eğitimde Kuram ve Uygulama, 19(2), 227-239. https://doi.org/10.17244/eku.1355304
60. Verma, S. (2024). Artificial Intelligence and Machine Learning in Aviation Industry. International Journal for Multidisciplinary Research. 6(2). 1-11. https://www.ijfmr.com/papers/2024/2/17562.pdf
61. Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 42(9), 1324-1337. https://doi.org/10.1080/0144929X.2022.2072768
62. Welsby, P., & Cheung, B. M. (2023). ChatGPT. Postgraduate Medical Journal, 99(1176), 1047-1048. https://doi.org/10.1093/postmj/qgad056
63. Xu, C. (2024). A Systematic Review of the Potential Influencing Factors for ChatGPT-Assisted Education. International Journal of Technology-Enhanced Education (IJTEE), 3(1), 1-19. DOI: 10.4018/IJTEE.339189
64. Yavaş, V. (2022). havacilikta dijitalleşme ve verimlilik ilişkisi üzerine bir içerik analizi. Verimlilik Dergisi, 225-237. https://doi.org/10.51551/verimlilik.974547
65. Yeşilyurt, S., & Çapraz, C. (2018). Ölçek geliştirme çalışmalarında kullanılan kapsam geçerliği için bir yol haritası. Erzincan Üniversitesi Eğitim Fakültesi Dergisi, 20(1), 251-264. https://doi.org/10.17556/erziefd.297741
66. Yılmaz, A. ve Kır, B. (2024). Chatgpt ve Bilimsel Araştırmalar: Yapay Zeka Kullanımının Nasıl Olması Gerektiği Üzerine Bir Literatür Taraması. 3. Bilsel International Ahlat Scientific Researches Congress, 8-9 Haziran, 297 Astana Yayınları. https://www.academia.edu/121649037/Chatgpt_ve_Bilimsel_Ara%C5%9Ft%C4%B1rmalar_Yapay_Zeka_Kullan%C4%B1m%C4%B1n%C4%B1n_Nas%C4%B1l_Olmas%C4%B1_Gerekti%C4%9Fi_%C3%9Czerine_Bir_Literat%C3%BCr_Taramas%C4%B1?utm_source=chatgpt.com
67. Yi, Y. (2021). Establishing the concept of AI literacy. Jahr–European Journal of Bioethics, 12(2), 353-368. https://doi.org/10.21860/j.12.2.8
68. Yükseköğretim Kurulu (2024, Mayıs). Üretken Yapay Zekânın Bilimsel Araştırma ve Yayınlarda Kullanımının Etik Boyutu. Yükseköğretim Kurumları Bilimsel Araştırma ve Yayın Faaliyetlerinde Üretken Yapay Zekâ Kullanımına Dair Etik Rehber. https://www.yok.gov.tr/Documents/2024/yapay-zeka-kullanimina-dair-etik-rehber.pdf
69. Zhai, C., & Wibowo, S. (2023). A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students’ interactional competence in the university. Computers and Education: Artificial Intelligence, 4, 100134. https://doi.org/10.1016/j.caeai.2023.100134