Generative Artificial Intelligence Acceptance Scale: A Validity and Reliability Study

References

• Ahmed, F., Shubeck, K., & Hu, X. (2023, July). ChatGPT in the generalized intelligent framework for tutoring. Proceedings of the 11th Annual Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym11) (p. 109). US Army Combat Capabilities Development Command-Soldier Center.
   Google Scholar
• Akbulut, Y. (2010). Sosyal bilimlerde SPSS uygulamaları [SPSS applications in social sciences]. İdeal Kültür Yayıncılık.
   Google Scholar
• Akiba, D., & Fraboni, M. C. (2023). AI-supported academic advising: Exploring ChatGPT’s current state and future potential toward student empowerment. Education Sciences, 13(9), 885. https://doi.org/10.3390/educsci13090885
   Web of Science ®Google Scholar
• Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. (2019). Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access, 7, 174673–174686. https://doi.org/10.1109/ACCESS.2019.2957206
   Web of Science ®Google Scholar
• Altalhi, M. (2021). Toward a model for acceptance of MOOCs in higher education: The modified UTAUT model for Saudi Arabia. Education and Information Technologies, 26(2), 1589–1605. https://doi.org/10.1007/s10639-020-10317-x
   Web of Science ®Google Scholar
• Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62.
   Google Scholar
• Balci, A. (2001). Sosyal bilimlerde araştırma. Yöntem, teknik ve ilkeler [Research in the social sciences. Methods, techniques and principles]. PegemA Yayınları.
   Google Scholar
• Baytak, A. (2023). The acceptance and diffusion of generative artificial intelligence in education: A literature review. Current Perspectives in Educational Research, 6(1), 7–18.
   Google Scholar
• Buyukozturk, S. (2010). Sosyal bilimler için veri analizi el kitabı [The data analysis handbook for social sciences]. Pegem Akademi Yayınları.
   Google Scholar
• Buyukozturk, S., Kilic Cakmak, E., Akgun, O. A., Karadeniz, S., & Ve Demirel, F. (2018). Bilimsel araştırma yöntemleri [Scientific research methods]. Pegem.
   Google Scholar
• Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Taylor & Francis.
   Google Scholar
• Chang, M., Walimuni, A. C., Kim, M. C., & Lim, H. S. (2022). Acceptance of tourism blockchain based on UTAUT and connectivism theory. Technology in Society, 71(2022), 102027. https://doi.org/10.1016/j.techsoc.2022.102027
   Google Scholar
• Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
   Web of Science ®Google Scholar
• Cokluk, O., Sekercioglu, G., & Buyukozturk, S. (2012). Sosyal bilimler için çok değişkenli istatistik: SPSS ve LISREL uygulamaları [Multivariate statistics for the social sciences: SPSS and LISREL applications]. Pegem Akademi Yayıncılık.
   Google Scholar
• Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444–452. https://doi.org/10.1007/s10956-023-10039-y
   Web of Science ®Google Scholar
• Creswell, J. W. (2012). Educational research: Planning, conducting and evaluating quantitative and qualitative research. Pearson.
   Google Scholar
• Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating academic answers generated using ChatGPT. Journal of Chemical Education, 100(4), 1672–1675. https://doi.org/10.1021/acs.jchemed.3c00087
   Web of Science ®Google Scholar
• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
   Google Scholar
• Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10.1038/nature14541
   PubMed Web of Science ®Google Scholar
• Hair, J. F., Anderson, R. E., Tatham, R. L., & Grablowsky, B. J. (1979). Multivariate data analysis. Pipe Books.
   Google Scholar
• Harshvardhan, G. M., Gourisaria, M. K., Pandey, M., & Rautaray, S. S. (2020). A comprehensive survey and analysis of generative models in machine learning. Computer Science Review, 38(2020), 100285. https://doi.org/10.1016/j.cosrev.2020.100285
   Web of Science ®Google Scholar
• 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. https://doi.org/10.21427/D7CF7R
   Google Scholar
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structural analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
   Web of Science ®Google Scholar
• Hwang, G. J., & Tu, Y. F. (2021). Roles and research trends of artificial intelligence in mathematics education: A bibliometric mapping analysis and systematic review. Mathematics, 9(6), 584. https://doi.org/10.3390/math9060584
   Web of Science ®Google Scholar
• Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1(2020), 100001. https://doi.org/10.1016/j.caeai.2020.100001
   Google Scholar
• Ilhan, M., & Cetin, B. (2014). Development of Classroom Assessment Environment Scale (CAES): Validity and reliability study. Education and Science, 39(176), 31–50. https://doi.org/10.15390/EB.2014.3334
   Web of Science ®Google Scholar
• Jang, Y., Choi, S., Kim, H., & Development, 27. (2022). and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gender and experience of AI education. Education and Information Technologies, 27(8), 11635–11667. https://doi.org/10.1007/s10639-022-11086-5
   Web of Science ®Google Scholar
• Jeon, J., Lee, S., & Choi, S. (2023). A systematic review of research on speech-recognition chatbots for language learning: Implications for future directions in the era of large language models. Interactive Learning Environments, 1–19. https://doi.org/10.1080/10494820.2023.2204343
   Google Scholar
• Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004
   Web of Science ®Google Scholar
• Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103(2023), 102274. https://doi.org/10.1016/j.lindif.2023.102274
   Web of Science ®Google Scholar
• Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77(2023), 101925. https://doi.org/10.1016/j.tele.2022.101925
   Web of Science ®Google Scholar
• Kline, R. B. (1994). An easy guide to factor analysis. Routledge.
   Google Scholar
• Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). Guilford Press.
   Google Scholar
• Küchemann, S., Steinert, S., Revenga, N., Schweinberger, M., Dinc, Y., Avila, K. E., & Kuhn, J. (2023). Physics task development of prospective physics teachers using ChatGPT. arXiv preprint arXiv:2304.10014.
   Google Scholar
• Li, B., Kou, X., & Bonk, C. J. (2023). Embracing the disrupted language teaching and learning field: Analyzing YouTube content creation related to ChatGPT. Languages, 8(3), 197. https://doi.org/10.3390/languages8030197
   Google Scholar
• Li, Z., Shi, L., Wang, J., Cristea, A. I., & Zhou, Y. (2023). Sim-GAIL: A generative adversarial imitation learning approach of student modelling for intelligent tutoring systems. Neural Computing and Applications, 35(34), 24369–24388. https://doi.org/10.1007/s00521-023-08989-w
   Google Scholar
• Lieto, A., Bhatt, M., Oltramari, A., & Vernon, D. (2018). The role of cognitive architectures in general artificial intelligence. Cognitive Systems Research, 48(2018), 1–3. https://doi.org/10.1016/j.cogsys.2017.08.003
   Web of Science ®Google Scholar
• Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
   Web of Science ®Google Scholar
• McKinsey. (2023). What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
   Google Scholar
• Miles, J., & Shevlin, M. (2007). A time and a place for incremental fit indices. Personality and Individual Differences, 42(5), 869–874. https://doi.org/10.1016/j.paid.2006.09.022
   Web of Science ®Google Scholar
• MIT Technology Review. (2022a). Generative AI is changing everything. But what’s left when the hype is gone? https://www.technologyreview.com/2022/12/16/1065005/generative-ai-revolution-art/
   Google Scholar
• MIT Technology Review. (2022b). Get ready for the next generation of AI. https://www.technologyreview.com/2022/10/03/1060561/get-ready-for-the-next-generation-of-ai/
   Google Scholar
• Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Penguin UK.
   Google Scholar
• Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2(2021), 100020. https://doi.org/10.1016/j.caeai.2021.100020
   Google Scholar
• Pallant, J. (2005). SPSS survival manual: A step by step guide to data analysis using SPSS for windows. Allen & Unwin.
   Google Scholar
• Park, Y. J. (2021). The future of digital surveillance: Why digital monitoring will never lose its appeal in a world of algorithm-driven AI. University of Michigan Press.
   Google Scholar
• Park, Y. J., & Jones-Jang, S. M. (2022). Surveillance, security, and AI as technological acceptance. AI & Society, 1–12. https://doi.org/10.1007/s00146-021-01331-9
   Google Scholar
• Prasad Agrawal, K. (2023). Towards adoption of generative AI in organizational settings. Journal of Computer Information Systems, 1–16. https://doi.org/10.1080/08874417.2023.2240744
   Google Scholar
• Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A. E., & Baneres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in higher education. Computers & Education, 182(2022), 104468. https://doi.org/10.1016/j.compedu.2022.104468
   Web of Science ®Google Scholar
• Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26(2), 582–599. https://doi.org/10.1007/s40593-016-0110-3
   Web of Science ®Google Scholar
• Ruiz-Rojas, L. I., Acosta-Vargas, P., De-Moreta-Llovet, J., & Gonzalez-Rodriguez, M. (2023). Empowering education with generative artificial intelligence tools: Approach with an instructional design matrix. Sustainability, 15(15), 11524. https://doi.org/10.3390/su151511524
   Web of Science ®Google Scholar
• Sallam, M., Salim, N., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R., & Hallit, S. (2023). Assessing attitudes and usage of ChatGPT in Jordan among health students: A validation study of the Technology Acceptance Model-Based Scale (TAME-ChatGPT). JMIR Medical Education, 9(2023), e48254. https://doi.org/10.2196/preprints.48254
   PubMedGoogle Scholar
• Sezer, B., & Yilmaz, R. (2019). Learning Management System Acceptance Scale (LMSAS): A validity and reliability study. Australasian Journal of Educational Technology, 35(3), 15–30. https://doi.org/10.14742/ajet.3959
   Web of Science ®Google Scholar
• Shidiq, M. (2023, May). The use of artificial intelligence-based chat-gpt and its challenges for the world of education; from the viewpoint of the development of creative writing skills. Proceeding of International Conference on Education, Society and Humanity (Vol. 1, No. 1, pp. 353–357).
   Google Scholar
• Sohn, K., & Kwon, O. (2020). Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. Telematics and Informatics, 47(2020), 101324. https://doi.org/10.1016/j.tele.2019.101324
   Web of Science ®Google Scholar
• Stevens, J. (1996). Applied multivariate statistics for the social sciences. Erlbaum.
   Google Scholar
• Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1–14. https://doi.org/10.1080/10494820.2023.2209881
   Google Scholar
• Sumer, N. (2000). Yapısal eşitlik modelleri [Structural equation models]. Turkish Psychological Articles, 3(6), 49–74.
   Google Scholar
• Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics. Pearson.
   Google Scholar
• Tamilmani, K., Rana, N. P., Wamba, S. F., & Dwivedi, R. (2021). The extended Unified Theory of Acceptance and Use of Technology (UTAUT2): A systematic literature review and theory evaluation. International Journal of Information Management, 57(2021), 102269. https://doi.org/10.1016/j.ijinfomgt.2020.102269
   Web of Science ®Google Scholar
• Tegmark, M. (2018). Life 3.0: Being human in the age of artificial intelligence. Vintage.
   Google Scholar
• Teng, Z., Cai, Y., Gao, Y., Zhang, X., & Li, X. (2022). Factors affecting learners’ adoption of an educational metaverse platform: An empirical study based on an extended UTAUT model. Mobile Information Systems, Special Issue (2022), 1–15. https://doi.org/10.1155/2022/5479215
   Web of Science ®Google Scholar
• Tezbaşaran, A. (1997). Likert tipi ölçek hazırlama kılavuzu [Likert type scale preparation guide]. Türk Psikologlar Derneği.
   Google Scholar
• The New York Times. (2023). A new area of A.I. booms, even amid the tech gloom. https://www.nytimes.com/2023/01/07/technology/generative-ai-chatgpt-investments.html
   Google Scholar
• Toh, S. Y., Ng, S. A., & Phoon, S. T. (2023). Accentuating technology acceptance among academicians: A conservation of resource perspective in the Malaysian context. Education and Information Technologies, 28(3), 2529–2545. https://doi.org/10.1007/s10639-022-11288-x
   PubMed Web of Science ®Google Scholar
• Ustun, A. B., Karaoglan-Yilmaz, F. G., & Yilmaz, R. (2023). Educational UTAUT-based virtual reality acceptance scale: A validity and reliability study. Virtual Reality, 27(2), 1063–1076. https://doi.org/10.1007/s10055-022-00717-4
   Web of Science ®Google Scholar
• Vărzaru, A. A. (2022). Assessing artificial intelligence technology acceptance in managerial accounting. Electronics, 11(14), 2256. https://doi.org/10.3390/electronics11142256
   Web of Science ®Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Wang, H., & Wang, S. (2010). User acceptance of mobile internet based on the unified theory of acceptance and use of technology: Investigating the determinants and gender differences. Social Behavior and Personality, 38(3), 415–426. https://doi.org/10.2224/sbp.2010.38.3.415
   Web of Science ®Google Scholar
• World Economic Forum. (2023). What is generative AI? An AI explains. https://www.weforum.org/agenda/2023/02/generative-ai-explain-algorithms-work/
   Google Scholar
• Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student attitudes towards Chat GPT: A technology acceptance model survey. International Educational Review, 1(1), 57–83. https://doi.org/10.58693/ier.114
   Google Scholar
• Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023a). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4(2023), 100147. https://doi.org/10.1016/j.caeai.2023.100147
   Google Scholar
• Yilmaz, R., & Karaoglan Yilmaz, F. G. (2023b). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2), 100005. https://doi.org/10.1016/j.chbah.2023.100005
   Google Scholar
• Yilmaz, R., Yurdugül, H., Karaoglan Yilmaz, F. G., Şahi̇n, M., Sulak, S., Aydin, F., Tepgeç, M., Müftüoğlu, C. T., & Ömer, O. R. A. L. (2022). Smart MOOC integrated with intelligent tutoring: A system architecture and framework model proposal. Computers and Education: Artificial Intelligence, 3(2022), 100092. https://doi.org/10.1016/j.caeai.2022.100092
   Google Scholar