Öğretmenlerin Yapay Zekâ Teknolojilerini Kabulünü Etkileyen Faktörler: Yapısal Eşitlik Modeli ile Öğretmen Bakış Açılarının Analizi

Year 2024, , 399 - 420, 31.12.2024

Özlem Gökçe Tekin

https://doi.org/10.52911/itall.1532218

Abstract

Yapay zekâ teknolojilerindeki hızlı ilerlemeler, eğitimde bu teknolojilerin kullanımının nasıl teşvik edileceğini gündeme getirmiştir. Öğretmenlerin yapay zekâ teknolojilerini kabulü bu bakımdan önemli bir yere sahiptir. Teknoloji Kabul Modeli'ne (TKM) dayanan bu çalışma, öğretmenlerin yapay zekâ teknolojilerini kabulünü etkileyen faktörleri araştırmaktadır. Bu amaçla TKM’ye Öz-yeterlik ve Kaygı eklenerek yapay zekâ teknolojisine yönelik beş yapılı bir yapısal model önerilmiştir. Verilerin toplanması için 21 maddeden oluşan bir ölçek hazırlanmıştır. 18 madde Doğrulayıcı Faktör Analizi ile doğrulanmıştır. Verilerin analizinde Yapısal Eşitlik Modeli kullanılmıştır. Önerilen modelde, Öz-yeterlik (ÖY), Yapay Zekâ Kaygısı (YZK), Algılanan Kullanım Kolaylığı (AKK), Algılanan Fayda (AF) ve Davranışsal Niyet (DN) ile ilgili 7 hipotez test edilmiştir. Hipotezlerden H1, H2 ve H7 ile anlamlı bir negatif etki; H3, H4 ve H6 ile ise anlamlı bir pozitif etki elde edilirken H5 doğrulanmamıştır. Öğretmenlerin Algılanan kullanım kolaylığının Algılanan faydası üzerindeki etkisinin (H3) ve Algılanan Faydasının Davranışsal niyeti üzerindeki etkisinin (H6) sırasıyla modeldeki en güçlü olumlu etkiler olduğu tespit edilmiştir. Yapay zekâ kaygısının Algılanan kullanım kolaylığının üzerindeki etkisinin (H2) ise en güçlü negatif etki oldğu tespit edilmiştir. Çalışmada öğretmenlerin öğretimde yapay zekâ teknolojilerini kullanmayı kabullerinin, öğretmenlerin yapay zekâya yönelik öz-yeterliği, yapay zekâ kaygısı ve algılanan faydası tarafından tahmin edilebilir olduğu tespit edilmiştir. Bu çalışmanın sonuçları TKM’nin genişletilmesine katkıda bulunmuştur. Bu çalışma Türkiye’de alanyazındaki önemli bir boşluğu doldurarak yapay zekâ teknolojilerini konu alan bir TKM çalışması sunmaktadır. Ayrıca çalışmanın sonuçları, eğitim teknolojilerinin kullanılmasında gelecekteki eğitim planlamalarına yardımcı olabilecek niteliktedir.

Keywords

Yapay zekâ kaygısı, Öz-yeterlik, Teknoloji kabul modeli, Yapısal eşitlik modeli, Öğretmenler

Factors Affecting Teachers' Acceptance of Artificial Intelligence Technologies: Analyzing Teacher Perspectives with Structural Equation Modeling

Abstract

Recent advances in artificial intelligence (AI) technologies have brought to the agenda how to encourage the use of these technologies in education. Teachers' acceptance of AI technologies has an important place. This study, based on the Technology Acceptance Model (TAM), investigates the factors affecting teachers' acceptance of AI technologies. A five-structure structural model for AI technology was proposed by adding Self-Efficacy and Anxiety to TAM. A trial form consisting of 21 items was prepared and 18 items were confirmed. Structural Equation Modeling (SEM) was used to analyze the data. In the proposed model, 7 hypotheses related to Self-Efficacy (SE), Artificial Intelligence Anxiety (AIA), Perceived Ease of Use (PEU), Perceived Utility (PU) and Behavioral Intention (BI) were tested. A significant negative effect was obtained with H1, H2 and H7; a significant positive effect was obtained with H3, H4 and H6, while H5 was not confirmed. The effect of teachers' perceived ease of use on perceived usefulness (H3) and the effect of perceived usefulness on behavioral intention (H6) were the strongest positive effects in the model. The effect of AI anxiety on perceived ease of use (H2) was the strongest negative effect. It was found that teachers' acceptance of using AI technologies in teaching is predictable by teachers' self-efficacy towards AI, AI anxiety and perceived usefulness. The results of this study contributed to the extension of TAM. This study presents a TAM study on AI technologies. In addition, the results can help future educational planning in the use of educational technologies.

Keywords

Artificial intelligence anxiety, Self-efficacy, Technology acceptance model, Structural equation model, Teachers

References

• Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior, 63, 75–90. https://doi.org/10.1016/j.chb.2016.05.014
• Ağmaz, R. F., & Ergulec, F. Perceptions of prospective teachers about artificial intelligence in education: A metaphor analysis. Necmettin Erbakan University Ereğli Faculty of Education Journal, 6(2), 589-605.
• Akbıyık, A., & Coşkun, E. (2013). A model for the acceptance and use of educational social software. AJIT-e: Academic Journal of Information Technology, 4(13), 39-62.
• Aktürk, A. O. & Delen, A. (2020). The relationship between teachers' technology acceptance levels and self-efficacy beliefs. Journal of Science, Education, Art and Technology (BEST Journal), 4(2), 67-80.
• Alenezi, A. R., Abdul Karim, A. M., & Veloo, A. (2010). An empirical investigation into the role of enjoyment, computer anxiety, computer self-efficacy and internet experience in influencing the students' intention to use e-learning: A case study from Saudi Arabian governmental universities. Turkish Online Journal of Educational Technology-TOJET, 9(4), 22-34.
• Antonenko, P., & Abramowitz, B. (2023). In-service teachers’(mis) conceptions of artificial intelligence in K-12 science education. Journal of Research on Technology in Education, 55(1), 64-78.
• Balıkçı, H. C., Alpsülün, M., & Hayoğlu, G. (2024). Determination of Teachers' Perceptions of Artificial Intelligence Concept: A Metaphor Analysis. Sakarya University Journal of Education, 14(2 (Special Issue-Artificial Intelligence Tools and Education)), 179-193.
• Bandura A. (1997). Self-Efficacy: The exercise of control. New York: Freeman.
• Büyüköztürk, Ş. (2014). Data analysis handbook for social sciences. Pegem Academy.
• Büyüköztürk, Ş., Çakmak, E. K., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2018). Scientific research methods. Pegem Academy.
• Çelik, İ. (2019). Investigation of instructors' acceptance of augmented reality technology as a course material. Van Yüzüncü Yıl University Institute of Educational Sciences. Master's thesis, Van.
• Çelik, S., Türkoğlu, T., Baydeniz, E., & Sandıkçı, M. (2023). Determination of factors affecting students’ behavioral intentions in the context of the Technology Acceptance Model, AHBVÜ Tourism Faculty Journal, 26 (1), 1-28.
• Çetin, M., & Aktaş, A. (2021). Artificial intelligence and future scenarios in education. OPUS International Journal of Social Research, Special Issue of Educational Sciences, 1-1. doi:10.26466/opus.911444
• Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: Combining technology acceptance model with external variables. Journal of Computing in Higher Education, 34, 844–867. https://doi.org/10.1007/s12528-022-09327-0
• Chatzoglou, P. D., Sarigiannidis, L., Vraimaki, E., & Diamantidis, A. (2009). Investigating greek employees' intention to use web-based training. Computers & Education, 53(3), 877e889. http://dx.doi.org/10.1016/j.compedu.2009.05.007
• Chen, H. R., & Tseng, H. F. (2012). Factors that influence acceptance of web based e learning systems for the in service education of junior high school teachers in Taiwan. Evaluation and Program Planning, 35(3), 398–406. https://doi.org/10.1016/j.evalprogplan.2011.11.007
• Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Institute of Electrical and Electronics Engineers Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
• Chen, S., Qiu, S., Li, H., Zhang, J., Wu, X., Zeng, W., & Huang, F. (2023). An integrated model for predicting pupils’ acceptance of artificially intelligent robots as teachers. Education and Information Technologies, 1-24.
• Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910-922.
• Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112(1), 155-159.
• Çokluk, Ö., Şekercioğlu, G., & Büyüköztürk, Ş. (2021). Multivariate statistics for social sciences: SPSS and LISREL, Pegem Academy.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
• Dülger, E. D., & Gümüşeli, A. İ. (2023). Views of school principals and teachers on using artificial intelligence in education. ISPEC International Journal of Social Sciences & Humanities, 7(1), 133-153.
• Durak, H. Y. (2018). Flipped learning readiness in teaching programming in middle schools: Modeling its relation to various variables. Journal of Computer Assisted Learning, 34(6), 939-959. https://doi.org/10.1111/jcal.12302
• Dursun, Y., & Kocagöz, E. (2010). Structural Equation Modeling and Regression: A Comparative Analysis. Erciyes University Journal of Faculty of Economics and Administrative Sciences, (35), 1-17.
• Fornell, C. ve Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
• Gurer, M. D. (2021). Examining technology acceptance of pre-service mathematics teachers in Turkey: A structural equation modeling approach. Education and Information Technologies, 26(4), 4709-4729.
• Güzey, C., Çakır, O., Athar, M. H., & Yurdaöz, E. (2023). Investigation of Trends in Research on Artificial Intelligence in Education. Journal of Information and Communication Technologies, 5(1), 67-78.
• Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2005). Multivariate data analysis (6th ed.). NY: Prentice Hall.
• Hamet, P. ve Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69(S), 36-40. https://doi.org/10.1016/j.metabol.2017.01.011
• Heerink, M., Krose, B., Evers, V., & Wielinga, B. (2009). Measuring acceptance of an assistive social robot: A suggested toolkit. In RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication (pp. 528–533). Toyama: IEEE. https://doi.org/10.1109/roman.2009.5326320
• Hoyle, R. H. (1995). Structural Equation Modeling: Concepts, Issues, and Applications. (First Edition). California: SAGE Publications, Inc.
• Işıksal, M., & Aşkar, P. (2003). Mathematics and computer self-efficacy perception scales for elementary school students. Hacettepe University Journal of Faculty of Education, 25(25).
• Kaiser, H.F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36.
• Karasar, N. (2016). Scientific research method: Concepts, principles, techniques. Nobel Academy Publishing.
• Kim, Y., & Glassman, M. (2013). Beyond search and communication: Development and validation of the Internet Self-efficacy Scale (ISS). Computers in Human Behavior, 29, 1421-1429.
• Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford Publications.
• Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology acceptance model integrating enjoyment, social interaction, and strength of the social ties. Telematics and Informatics, 39, 37–48. https://doi.org/10.1016/j.tele.2018.12.006
• Ma, S., & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 1-18. https://doi.org/10.1080/02188791.2024.2305155
• Mac Callum, K., Jeffrey, L., & NA, K. (2014). Factors impacting Teachers’ adoption of mobile learning. Journal of Information Technology Education: Research, 13, 141–162.
• Manav, F. (2011). The concept of anxiety. Journal of Social Sciences, 5(9), 201-211.
• Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, A Multidisciplinary Journal, 4, 599−620.
• Nabiyev, V. & Erümit, A. K. (2020). Yapay zekânın temelleri. İçinde V. Nabiyev ve A. K. Erümit (Edl.), Eğitimde yapay zekâ kuramdan uygulamaya (ss. 2-37), Pegem Akademi Yayınları.
• Ng, D. T. K., Luo, W. Y., Chan, H. M. Y., & Chu, S. K. W. (2022). An examination on primary students’ development in AI literacy through digital story writing. Computers & Education: Artificial Intelligence, 100054.
• Ng, D. T. K., 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.
• Nuangchalerm, P., & Prachagool, V. (2023). AI-driven learning analytics in STEM education. International Journal of Research in STEM Education, 5(2), 77-84.
• Palinkas, L. A., Horwitz, S. M., Green, C. A., Wisdom, J. P., Duan, N., & Hoagwood, K. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y
• Raykov, T., & Marcoulides, G. A. (2000). A first course in structural equation modeling, Mahwah, NJ: Lawrence Erlbaum Associates, 238 p.
• Şahin, F., & Şahin, Y. L. (2021). Examining the acceptance of e-learning systems during the pandemic: The role of compatibility, enjoyment and anxiety. International Technology and Education Journal, 5(1), 01-10.
• Scherbelleh-Engel, K., Moosbrugger, H., & Muller, 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.
• Schunk, D. H. (1981). Modeling and attributional effects on children's achievement: A self-efficacy analysis. Journal of Educational Psychology, 73(1), 93. https://psycnet.apa.org/doi/10.1037/0022-0663.73.1.93
• Seyrek, M., Yıldız, S., Emeksiz, H., Şahin, A., & Türkmen, M. T. (2024). Teachers' perceptions of artificial intelligence in education. International Journal of Social and Humanities Sciences Research (JSHSR), 11(106), 845-856.
• Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/10.1016/j.compedu.2008.08.006
• Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A Structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142.
• Ursavaş, Ö. F. (2014). Modeling teachers' behaviors towards using information technologies. Unpublished Doctoral Dissertation. Gazi University, Ankara.
• Ursavaş, Ö. F., Yalçın, Y., & Bakır, E. (2019). The effect of subjective norms on preservice and in-service teachers’ behavioral intentions to use technology: A multigroup multimodel study. British Journal of Educational Technology, 50(5), 2501-2519.
• Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation and emotion into the Technology Acceptance Model. Information Systems Research, 11(4), 342–365.
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
• Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x
• Wang, B., Rau, P., & Yuan, T. (2022). 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
• Wang, Y. Y., & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2019.1674887
• 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
• Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors Affecting the Adoption of AI-Based Applications in Higher Education: An Analysis of Teachers’ Perspectives Using Structural Equation Modeling. Educational Technology & Society, 24(3), 116–129.
• Wong, G. K. W. (2015). Understanding technology acceptance in pre-service teachers of primary mathematics in Hong Kong. Australasian Journal of Educational Technology, 31(6), 713–735.
• Yeşilyurt, E., Ulaş, A. H., & Akan, D. (2016). Teacher self-efficacy, academic self-efficacy, and computer self-efficacy as predictors of attitude toward applying computer-supported education. Computers in Human Behavior, 64, 591-601.
• Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49.
• Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25(1), 82-91. https://doi.org/10.1006/ceps.1999.1016