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Scale of Artificial Intelligence Awareness and Usage Tendencies for Middle School Students: Development, Validity, and Reliability Study

Yıl 2025, Sayı: 64, 2451 - 2475, 30.06.2025

Yavuz Yaman , Süleyman Kahraman , Ramazan Zengin

https://doi.org/10.53444/deubefd.1611893

Öz

This study aimed to develop a four-dimensional scale to measure middle school students' awareness of and tendencies toward artificial intelligence (AI). The Middle School Artificial Intelligence Awareness and Usage Tendency Scale (YZFKÖ) consists of four dimensions: Basic AI Knowledge, AI Usage Areas, AI Anxiety, and AI Challenges.
The study was conducted using literature review, expert opinions, pilot application, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). The EFA and CFA results revealed that the four-factor structure explained 52.05% of the total variance and that the goodness-of-fit indices were at an acceptable level (KMO = .924; χ²/df = 2.057; CFI = .935; RMSEA = .043).
The Cronbach Alpha coefficient was determined as α = .870 for the entire scale, and the sub-dimensions ranged between α = .743 and α = .914. These findings indicate that the scale is a valid, reliable, and consistent measurement tool.
The developed scale provides teachers, curriculum developers, and education policymakers with a valid and reliable tool for measuring students' levels of AI awareness and usage tendencies.

Anahtar Kelimeler

Artificial intelligence awareness Artificial intelligence usage trends Middle school Scale development Artificial intelligence in education

Kaynakça

  • Aldosari, S. A. (2020). The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 9(3), Article 145. https://doi.org/10.5430/ijhe.v9n3p145
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  • Brown, T. A. (2015). Confirmatory factor analysis for applied research, Second Edition. Guilford Publications. Buchanan, B. G., & Sandham, J. (2022). AI is changing the world. Science, 375(6586), 1190-1193.
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  • Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS – Meta AI Literacy Scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
  • Carpenter, S. (2017). Ten steps in scale development and reporting: A Guide for Researchers. Communication Methods and Measures, 12(1), 25–44. https://doi.org/10.1080/19312458.2017.1396583
  • Çayır, A. (2023). A Literature Review on the Effect of Artificial Intelligence on Education. İnsan ve Sosyal Bilimler Dergisi, 6(2), 276–288. https://doi.org/10.53048/johass.1375684
  • 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
  • Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2024). Explicating AI Literacy of Employees at Digital Workplaces. IEEE Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/tem.2021.3138503
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  • 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
  • Hair, J., Anderson, R., Black, B., & Babin, B. (2016). Multivariate data analysis. Pearson Higher Ed.
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  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
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Ortaokul Öğrencileri İçin Yapay Zekâ Farkındalık ve Kullanım Eğilimleri Ölçeği: Geliştirme, Geçerlik ve Güvenirlik Çalışması

Yıl 2025, Sayı: 64, 2451 - 2475, 30.06.2025

Yavuz Yaman , Süleyman Kahraman , Ramazan Zengin

https://doi.org/10.53444/deubefd.1611893

Öz

Bu çalışma, ortaokul öğrencilerinin yapay zekâ (YZ) farkındalığı ve kullanım eğilimlerini ölçmek amacıyla dört boyutlu bir ölçek geliştirmeyi amaçlamaktadır. Ortaokul Yapay Zekâ Farkındalık ve Kullanım Eğilimleri Ölçeği (YZFKÖ), YZ Temel Bilgi, YZ Kullanım Alanları, YZ Kaygı ve YZ Zorlanma olmak üzere dört boyuttan oluşmaktadır.
Ölçeğin geliştirme sürecinde literatür taraması, uzman görüşleri, pilot uygulama, Açıklayıcı Faktör Analizi (AFA) ve Doğrulayıcı Faktör Analizi (DFA) gerçekleştirilmiştir. AFA ve DFA sonuçları, dört faktörlü yapının toplam varyansın %52,05'ini açıkladığını ve uyum iyiliği indekslerinin kabul edilebilir düzeyde olduğunu göstermiştir (KMO = ,924; χ²/df = 2,057; CFI = ,935; RMSEA = ,043).
Cronbach Alfa katsayısı tüm ölçek için α = ,870 olarak belirlenmiş, alt boyutlar için ise α değerlerinin ,743 ile ,914 arasında değiştiği görülmüştür. Bu bulgular, ölçeğin geçerli, güvenilir ve tutarlı bir ölçüm aracı olduğunu ortaya koymaktadır.
Bu çalışmada geliştirilen ölçek, öğretmenler, eğitim programı geliştiricileri ve eğitim politikacıları için öğrencilerin YZ farkındalık ve kullanım eğilimlerini ölçmede geçerli ve güvenilir bir araç sunmaktadır.
Anahtar Kelimeler: Yapay zekâ farkındalığı, Yapay zekâ kullanım eğilimleri, Ortaokul, Ölçek geliştirme, Eğitimde yapay zekâ

Anahtar Kelimeler

Yapay zekâ farkındalığı Yapay zekâ kullanım eğilimleri Ölçek geliştirme

Etik Beyan

İnsan katılımcıların yer aldığı çalışmalarda gerçekleştirilen tüm prosedürler, kurumsal ve/veya ulusal araştırma komitesinin etik standartlarına ve 1964 Helsinki Bildirgesi ve sonraki değişikliklerine veya benzer etik standartlara uygundur. Bu çalışma için etik onay, İstanbul Aydın Üniversitesi Eğitim Bilimleri Etik Kurulu'ndan alınmıştır.

Destekleyen Kurum

YOK

Teşekkür

YOK

Kaynakça

  • Aldosari, S. A. (2020). The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 9(3), Article 145. https://doi.org/10.5430/ijhe.v9n3p145
  • Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L. (2018). Best practices for developing and validating scales for health, social, and behavioral research: A primer. Frontiers in Public Health, 6, 149, https://doi.org/10.3389/fpubh.2018.00149
  • Brown, T. A. (2015). Confirmatory factor analysis for applied research, Second Edition. Guilford Publications. Buchanan, B. G., & Sandham, J. (2022). AI is changing the world. Science, 375(6586), 1190-1193.
  • Büyüköztürk, Ş. (2018). Sosyal bilimler için veri analizi el kitabı. Ankara: Pegem Akademi
  • Calvani, A., Cartelli, A., Fini, A., & Ranieri, M. (2008). Models and instruments for assessing digital competence at school. Journal of E-learning and Knowledge Society, 4(3), 183-193.
  • Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS – Meta AI Literacy Scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
  • Carpenter, S. (2017). Ten steps in scale development and reporting: A Guide for Researchers. Communication Methods and Measures, 12(1), 25–44. https://doi.org/10.1080/19312458.2017.1396583
  • Çayır, A. (2023). A Literature Review on the Effect of Artificial Intelligence on Education. İnsan ve Sosyal Bilimler Dergisi, 6(2), 276–288. https://doi.org/10.53048/johass.1375684
  • 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
  • Cetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2024). Explicating AI Literacy of Employees at Digital Workplaces. IEEE Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/tem.2021.3138503
  • Chetty, K., Ntshayintshayi, N., Tlou, A., Saal, P., Moosa, T., Mgebisa, L., ... & Mdlulwa, N. (2023). AI skills development roadshow with the African Leadership University.
  • Chiu, T. K. F., Chai, C. S., & Tan, C. M. (2021). AI literacy: Definitions and design considerations. Computers & Education: Artificial Intelligence, 2, 100017. https://doi.org/10.1016/j.caeai.2021.100017
  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.
  • Costello, A. B. & Osborne, J., (2005) “Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis”, Practical Assessment, Research, and Evaluation 10(1): 7. doi: https://doi.org/10.7275/jyj1-4868
  • Cuomo, S., Biagini, G., & Ranieri, M. (2022). Artificial intelligence literacy, che cos’è e come promuoverla: Dall’analisi della letteratura ad una proposta di framework. Media Education, 13(2), 161–172, https://doi.org/10.36253/me-13374 Çakır, F. S. (2018). YAPAY SİNİR AĞLARI-Matlab Kodları ve Matlab Toolbox Çözümleri. Nobel Akademik Yayıncılık.
  • DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). Thousand Oaks, CA: SAGE.
  • Floridi, L. (2019). What the near future of artificial intelligence could be. Ethics, Governance, and Policies in Artificial Intelligence, 379-394, https://doi.org/10.1007/s13347-019-00345-y
  • Gibson, D., Kovanovic, V., Ifenthaler, D., Dexter, S., & Feng, S. (2023). Learning theories for artificial intelligence promoting learning processes. British Journal of Educational Technology, 54(5), 1125–1146. https://doi.org/10.1111/bjet.13341.
  • Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS- 4): A brief measure of general attitude toward artificial intelligence. Front. Psychol. 14:1191628. doi: 10.3389/fpsyg.2023.1191628
  • 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
  • Hair, J., Anderson, R., Black, B., & Babin, B. (2016). Multivariate data analysis. Pearson Higher Ed.
  • Hertzog M. A. (2008). Considerations in determining sample size for pilot studies. Research in nursing & health, 31(2), 180–191. https://doi.org/10.1002/nur.20247
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
  • Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564–569. https://doi.org/10.1007/s11528-019-00375-5
  • Huang, J., Saleh, S., & Liu, Y. (2021). A review on artificial intelligence in education. Academic Journal of Interdisciplinary Studies, 10(3), 206. https://doi.org/10.36941/ajis-2021-0077
  • Kasinidou, M. (2023, March). Promoting AI literacy for the public. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education (Vol. 2, p. 1237). ACM. https://doi.org/10.1145/3545947.3573292
  • Kline, R. B. (2023). Principles and Practice of Structural Equation Modeling, Fourth edition. Guilford Publications.
  • Kong, S. C., Lai, M., Sun, D., Luo, H., & Wong, K. H. (2021). Development and validation of an AI literacy scale for university students. International Journal of Artificial Intelligence in Education, 31(4), 725–747. https://doi.org/10.1007/s40593-021-00251-7
  • Kyriazos, T. (2018) Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General. Psychology, 9, 2207-2230. https://doi.org/10.4236/psych.2018.98126.
  • 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
  • Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
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  • Moodi Ghalibaf, A., Moghadasin, M., Emadzadeh, A., & Mastour, H. (2023). Psychometric properties of the persian version of the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS). BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04553-1
  • Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
  • Ng, D., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2023). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411
  • Organisation for Economic Co-operation and Development. (2024). Artificial intelligence in education: A guide for policymakers. Paris: OECD Publishing.
  • Pala, Ş. M., & Başıbüyük, A. (2020). 10-12 yaş grubu öğrencileri için dijital okuryazarlık ölçeği geliştirme çalışması. Akdeniz Eğitim Araştırmaları Dergisi, 14(33), 542–565. https://doi.org/10.29329/mjer.2020.272.25
  • Pinski, M., & Benlian, A. (2023). AI literacy—Towards measuring human competency in artificial intelligence. In T. X. Bui (Ed.), Proceedings of the 56th Hawaii International Conference on System Sciences (pp. 165–174). ScholarSpace.
  • Pişkin, M., Atik, G., Çınkır, Ş., Öğülmüş, S., Babadoğan, C., & Çokluk, Ö. (2014). The development and validation of the Teacher Violence Scale. Eurasian Journal of Educational Research, 56(56), 69–88. https://doi.org/10.14689/ejer.2014.56.3
  • Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. https://doi.org/10.1002/nur.20147
  • Rakuasa, H. (2023). Integration of artificial intelligence in geography learning: challenges and opportunities.
  • Sinergi International Journal of Education, 1(2), 75-83. https://doi.org/10.61194/education.v1i2.71
  • Raykov, T., & Hancock, G. R. (2005). Examining change in maximal reliability for multiple‐component measuring instruments. British Journal of Mathematical and Statistical Psychology, 58(1), 65–82. https://doi.org/10.1348/000711005x38753
  • Richter, S., Giroux, M., Piven, I., Sima, H., & Dodd, P. (2024). A Constructivist Approach to Integrating AI in Marketing Education: Bridging Theory and Practice. Journal of Marketing Education, 0(0). https://doi.org/10.1177/02734753241288876
  • Rodríguez-García, Á. M., Moreno-León, J., Román-González, M., & Robles, G. (2021). Introducing artificial intelligence in computer science classrooms: An experience report. Informatics, 8(3), 54.
  • Roshanaei, M., Olivares, H., & Lopez, R. R. (2023). Harnessing AI to foster equity in education: opportunities, challenges, and emerging strategies. Journal of Intelligent Learning Systems and Applications, 15(04), 123-143. https://doi.org/10.4236/jilsa.2023.154009
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  • Wang, Y.-Y., & Chuang, Y.-W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29(4), 4785–4808. https://doi.org/10.1007/s10639-023-12015-w
  • Wang, Y.-Y., & Wang, Y.-S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. https://doi.org/10.1080/10494820.2019.1674887
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  • Yang, Y., & Xu, X. (2024). Research on artificial intelligence literacy level and its influencing factors of high school students. International Journal of Social Science and Research. https://doi.org/10.58531/ijssr/2/2/1
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  • Zhou, X., Van Brummelen, J., & Lin, P. (2020). Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2009.10228
Toplam 68 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ölçek Geliştirme, Öğretim Teknolojileri
Bölüm Makaleler
Yazarlar

Yavuz Yaman 0000-0002-4837-9959

Süleyman Kahraman 0000-0002-8223-4614

Ramazan Zengin 0000-0001-8938-9486

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 2 Ocak 2025
Kabul Tarihi 20 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 64

Kaynak Göster

APA Yaman, Y., Kahraman, S., & Zengin, R. (2025). Ortaokul Öğrencileri İçin Yapay Zekâ Farkındalık ve Kullanım Eğilimleri Ölçeği: Geliştirme, Geçerlik ve Güvenirlik Çalışması. Dokuz Eylül Üniversitesi Buca Eğitim Fakültesi Dergisi(64), 2451-2475. https://doi.org/10.53444/deubefd.1611893
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