TY  - JOUR
T1  - Development of the Artificial Intelligence Perception and Attitude Scale (AIPAS)
TT  - Yapay Zekâ Algı ve Tutum Ölçeğinin Geliştirilmesi (YAZAT-24)
AU  - Dinler, Hızır
PY  - 2025
DA  - October
Y2  - 2025
DO  - 10.14686/buefad.1602673
JF  - Bartın University Journal of Faculty of Education
JO  - BUEFAD
PB  - Bartin University
WT  - DergiPark
SN  - 1308-7177
SP  - 1283
EP  - 1304
VL  - 14
IS  - 4
LA  - en
AB  - Given the increasing integration of artificial intelligence (AI) into daily life, understanding public perception and attitudes towards these technologies is crucial. This research introduces the ‘Artificial Intelligence Perception and Attitude Scale’ (AIPAS), developed to comprehensively assess individuals' perceptions and attitudes towards AI technologies. The instrument evaluates four distinct yet interconnected sub-dimensions: Positive Perception (evaluating optimistic views and perceived benefits), Negative Perception (assessing concerns and anxieties), Generative Media Use (gauging interaction with AI in creative media generation), and Chatbot Interaction (focusing on experiences with conversational AI). Validation and reliability testing involved 1,600 participants from Türkiye. Exploratory Factor Analysis revealed a clear four-factor structure with 24 items, accounting for 73.59% of the total variance. Confirmatory Factor Analysis affirmed this structure, yielding strong fit indices (x²/sd = 1.54, RMSEA = .07, CFI = .97, TLI = .97). The scale’s overall internal consistency, measured by Cronbach's alpha, was excellent at .93, with sub-dimension alphas ranging robustly between .90 and .96. These findings demonstrate that AIPAS is a reliable and valid tool, offering a nuanced instrument for examining the multifaceted nature of AI-related perceptions and attitudes, particularly within the Turkish context, and providing a valuable resource for future research and policy-making.
KW  - Artificial intelligence
KW  - AI perception
KW  - AI attitude
KW  - scale development
KW  - AI media tools
KW  - AI Chatbots
KW  - Generative AI
N2  - Yapay zekânın günlük hayata artan entegrasyonu göz önüne alındığında, bu teknolojilere yönelik toplumsal algı ve tutumların anlaşılması büyük önem taşımaktadır. Bu çalışmanın amacı, bireylerin yapay zekâ teknolojilerine yönelik algı ve tutumlarını kapsamlı bir şekilde ölçmek için “Yapay Zekâ Algı ve Tutum Ölçeği”ni (YAZAT) geliştirmektir. Ölçek, birbiriyle ilişkili ancak ayrı dört alt boyutu değerlendirmektedir: Olumlu Algı (iyimser görüşleri ve algılanan faydaları değerlendiren), Olumsuz Algı (endişeleri ve kaygıları ölçen), Üretken Medya Kullanımı (yaratıcı medya üretiminde yapay zekâ ile etkileşimi belirleyen) ve Sohbet Robotu Etkileşimi (sohbet tabanlı yapay zekâ deneyimlerine odaklanan). Geçerlilik ve güvenilirlik analizleri, Türkiye'den 1600 katılımcıyla gerçekleştirilmiştir. Açımlayıcı Faktör Analizi, 24 madde ve dört faktörden oluşan net bir yapının toplam varyansın %73,59'unu açıkladığını ortaya koymuştur. Doğrulayıcı Faktör Analizi bu yapıyı doğrulamış ve güçlü uyum indeksleri sunmuştur (x²/sd = 1.54, RMSEA= .07, CFI= .97, TLI= .97). Ölçeğin Cronbach alfa ile ölçülen genel iç tutarlılığı .93 ile mükemmel düzeydedir; alt boyut alfa katsayıları ise .90 ile .96 arasında güçlü bir aralıkta değişmektedir. Bulgular, YAZAT'ın, özellikle Türkiye bağlamında yapay zekâ ile ilgili algı ve tutumların çok yönlü doğasını incelemek için incelikli bir araç sunan ve gelecekteki araştırmalar ile politika oluşturma süreçleri için değerli bir kaynak teşkil eden geçerli ve güvenilir bir ölçek olduğunu göstermektedir.
CR  - Allport, G. W. (1935). Attitudes. In C. Murchison (Ed.), A handbook of social psychology (pp. 798–844). Clark University Press.
CR  - Amankwah-Amoah, J., Abdalla, S., Mogaji, E., Elbanna, A., & Dwivedi, Y. K. (2024). The impending disruption of creative industries by generative AI: Opportunities, challenges, and research agenda. International Journal of Information Management, 79, 102759. https://doi.org/10.1016/j.ijinfomgt.2024.102759
CR  - Bentler, P. M., & Bonnet, D. C. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88(3), 588-606. https://doi.org/10.1037/0033-2909.88.3.588
CR  - Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). Guilford publications.
CR  - Büyüköztürk, Ş. (2005). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorumu. Pegem Akademi.
CR  - Byrne, B. M. (2013). Structural equation modelling with AMOS: Basic concepts, applications, and programming (3rd ed.). Routledge. https://doi.org/10.4324/9781410600219
CR  - Choi, J.-I., Yang, E., & Goo, E.-H. (2024). The effects of an ethics education program on artificial intelligence among middle school students: Analysis of perception and attitude changes. Applied Sciences, 14(4), 1588. https://doi.org/10.3390/app14041588
CR  - Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
CR  - Dinler, H. (2024). Intelligent educational robots in early childhood education. In S. Papadakis & G. Lampropoulos (Eds.), Intelligent educational robots: Toward personalised learning environments. De Gruyter STEM. https://doi.org/10.1515/9783111352695-010
CR  - Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Harcourt brace Jovanovich college publishers.
CR  - Fakhri, M. M., Jannah, D. M., Isma, A., Dewantara, H., & Nirmala S., A. (2025). From Ethics to Impact: Modeling the Role of AI Perception Dynamics in the Relationship Between Ethics AI Practices, AI-Driven Societal Impact, and AI Behavioral Analysis. Journal of Applied Science, Engineering, Technology, and Education, 7(1), 56-68. https://doi.org/10.35877/454RI.asci3802
CR  - Fife-Schaw, C. (2000). Introduction to structural equation modelling. Research methods in psychology, 2, 397-413.
CR  - Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
CR  - Holmes, W. (2019). Artificial intelligence in education. In Encyclopedia of education and information technologies (pp. 1-16). Springer, Cham. https://doi.org/10.1007/978-3-319-60013-0_107-1
CR  - Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 1-23. https://doi.org/10.1007/s40593-021-00239-1
CR  - Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
CR  - Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modelling: a Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
CR  - Karasar, N. (2005). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler [Scientific research method: Concepts, principles, techniques]. Istanbul: Nobel Publications.
CR  - Kshirsagar, P. R., Jagannadham, D. B. V., Alqahtani, H., Noorulhasan Naveed, Q., Islam, S., Thangamani, M., & Dejene, M. (2022). Human intelligence analysis through perception of AI in teaching and learning. Computational Intelligence and Neuroscience, 2022(1), 9160727. https://doi.org/10.1155/2022/9160727
CR  - Kline, R. B. (2023). Principles and practice of structural equation modelling (6th ed.). Guilford publications.
CR  - Krägeloh, C. U., Melekhov, V., Alyami, M. M., & Medvedev, O. N. (2025). Artificial Intelligence Attitudes Inventory (AIAI): development and validation using Rasch methodology. Current Psychology, 1-13. https://doi.org/10.1007/s12144-025-08009-1
CR  - Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the “Scale for the assessment of non-experts’ AI literacy”–An exploratory factor analysis. Computers in Human Behavior Reports, 12, 100338. https://doi.org/10.1016/j.chbr.2023.100338
CR  - Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
CR  - Luckin, R & Holmes, W. (2016) Intelligence Unleashed: An argument for AI in Education. UCL Knowledge Lab: London, UK.
CR  - Ma, S., & Chen, Z. (2024). The Development and Validation of the Artificial Intelligence Literacy Scale for Chinese College Students (AILS-CCS). IEEE Access. https://doi.org/10.1109/ACCESS.2024.3468378
CR  - Monib, W. K., Qazi, A., & Mahmud, M. M. (2025). Exploring learners’ experiences and perceptions of ChatGPT as a learning tool in higher education. Education and information technologies, 30(1), 917-939. https://doi.org/10.1007/s10639-024-13065-4
CR  - Mulgan, T. (2016). Superintelligence: Paths, dangers, strategies. The Philosophical Quarterly, 66(262), 196–203, https://doi.org/10.1093/pq/pqv034.
CR  - Özdamar, K. (2011). Statistical data analysis with package programmes-1 (7th edition). Kaan Publish.
CR  - Rigdon, E. E. (1996). CFI versus RMSEA: A comparison of two fit indexes for structural equation modelling. Structural Equation Modelling: A Multidisciplinary Journal, 3(4), 369-379. https://doi.org/10.1080/10705519609540052
CR  - Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach (4th ed.). Pearson Education Limited.
CR  - Schepman, A., & Rodway, P. (2020). Initial validation of the General Attitudes Towards Artificial Intelligence Scale. Computers in Human Behaviour Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014
CR  - Schiavo, G., Businaro, S., & Zancanaro, M. (2024). Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence. Technology in Society, 77, 102537. https://doi.org/10.1016/j.techsoc.2024.102537
CR  - Sindermann, C., Yang, H., Elhai, J. D., Yang, S., Quan, L., Li, M., & Montag, C. (2022). Acceptance and Fear of Artificial Intelligence: associations with personality in a German and a Chinese sample. Discover Psychology, 2(1), 8. https://doi.org/10.1007/s44202-022-00020-y
CR  - Singh, N., Pandey, A., Tikku, A. P., Verma, P., & Singh, B. P. (2023). Attitude, perception and barriers of dental professionals towards artificial intelligence. Journal of Oral Biology and Craniofacial Research, 13(5), 584–588. https://doi.org/10.1016/j.jobcr.2023.06.006
CR  - Tabachnick, B. G., & Fidell, L. S. (2015). Using multivariate statistics (6th ed.). Pearson Education.
CR  - Tarafdar, M., Page, X., & Marabelli, M. (2023). Algorithms as co‐workers: Human algorithm role interactions in algorithmic work. Information Systems Journal, 33(2), 232-267. https://doi.org/10.1111/isj.12389
CR  - Tavşancıl, E., & Keser, H. (2002). Development of an attitude scale for internet use. Educational Sciences and Practice, 1(1), 79-97.
CR  - Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. https://doi.org/10.1007/BF02291170
CR  - Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
CR  - Wakunuma, K., & Eke, D. (2024). Africa, ChatGPT, and generative AI systems: Ethical benefits, concerns, and the need for governance. Philosophies, 9(3), 80. https://doi.org/10.3390/philosophies9030080
CR  - 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
CR  - Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?. International journal of educational technology in higher education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
CR  - Zhang, B., & Dafoe, A. (2019). Artificial Intelligence: American Attitudes and Trends. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3312874
UR  - https://doi.org/10.14686/buefad.1602673
L1  - https://dergipark.org.tr/en/download/article-file/4445687
ER  -