Abstract
With the advancement of artificial intelligence (AI) and machine learning (ML) techniques, attitudes towards these two fields have begun to gain importance in different professions. One of the affected professions is undoubtedly the teaching profession. Increasing the levels of concern for artificial intelligence and attitudes towards machine learning has become important in order to adapt to potential technologies that will be used. The purpose of this study is to examine the anxiety related to AI and the attitudes towards ML among teacher candidates of different ages, genders, and fields. This study investigates the relationships between sub-dimensions of anxiety towards artificial intelligence and attitudes towards machine learning, as well as to identify differences in these sub-dimensions among gender, age, and department. The findings suggest that although teacher candidates from different disciplines, ages, and genders do not have any concerns regarding learning about artificial intelligence, they do express anxiety about the impact of artificial intelligence on employment rates and social life. The results of this study can be beneficial for developing instructional programs that focus on AI in the long run, considering factors such as age, personal experience, gender, and field-specific elements.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Change history
22 September 2023
The ORCID of the first author has been corrected.
References
Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zeka kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125–1146.
Al Saad, M. M., Shehadeh, A., Alanazi, S., Alenezi, M., Abu alez, A., Eid, H., Alfaouri, M. S., Aldawsari, S., & Alenezi, R. (2022). Medical students’ knowledge and attitude towards Artificial Intelligence: An online survey. The Open Public Health Journal, 15(1), https://doi.org/10.2174/18749445-V15-E2203290.
Albarrán-Lozano, I., Molina, J. M., & Gijón, C. (2021). Perception of Artificial Intelligence in Spain. Telematics and Informatics. https://doi.org/10.1016/J.TELE.2021.101672. 63.
Baigi, S. F. M., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023). Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review. Health Science Reports, 6(3), https://doi.org/10.1002/HSR2.1138.
Başer, A., Altuntaş, S. B., Kolcu, G., & Özceylan, G. (2021). Artificial Intelligence anxiety of Family Physicians in Turkey. Progress in Nutrition, 23(S2), e2021275–e2021275. https://doi.org/10.23751/PN.V23IS2.12003.
Bonneau-Diesce, J., & Chan, A. (2022). Will artificial intelligence ever be a threat to humankind? Journal of Student Research, 11(2), https://doi.org/10.47611/jsrhs.v11i2.2511.
Chalutz Ben-Gal, H. (2023). Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients’ gender, age and health awareness? A two-phase pilot study. Frontiers in Public Health, 10, https://doi.org/10.3389/FPUBH.2022.931225.
Cohen, A., Soffer, T., & Henderson, M. (2022). Students’ use of technology and their perceptions of its usefulness in higher education: International comparison. Journal of Computer Assisted Learning, 38(5), 1321–1331. https://doi.org/10.1111/JCAL.12678.
Cortina, J. M. (1993). What is Coefficient Alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98–104. https://doi.org/10.1037/0021-9010.78.1.98.
Dahlin, E. (2019). Are Robots Stealing our Jobs? Socius, 5, 1–14. https://doi.org/10.1177/2378023119846249.
Elliott, D., & Soifer, E. (2022). AI Technologies, privacy, and security. Frontiers in Artificial Intelligence, 5, 60. https://doi.org/10.3389/FRAI.2022.826737/BIBTEX.
Hopcan, S., Polat-Hopcan, E., & Türkmen, G. (2021). Validity and reliability study of a turkish form of the machine learning attitude scale. Research on Education and Psychology, 5(2), 246–266. https://doi.org/10.54535/REP.1017070.
Huang, X. (2021). Aims for cultivating students’ key competencies based on artificial intelligence education in China. Education and Information Technologies, 26, 5127–5147. https://doi.org/10.1007/s10639-021-10530-2.
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Kaya, D. (2022). M. The roles of personality traits, AI anxiety, and demographic factors in attitudes toward Artificial Intelligence. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2022.2151730.
Kharb, L., & Singh, P. (2021). Role of machine learning in Modern Education and Teaching. Impact of AI Technologies on Teaching Learning and Research in Higher Education, 99–123. https://doi.org/10.4018/978-1-7998-4763-2.ch006.
Lee, Y. (2019). An analysis of the influence of block-type programming language-based artificial intelligence education on the learner’s attitude in artificial intelligence. Journal of The Korean Association of Information Education, 23(2), 189–196.
Lemay, D. J., Basnet, R. B., & Doleck, T. (2020). Fearing the Robot Apocalypse: Correlates of AI anxiety. International Journal of Learning Analytics and Artificial Intelligence for Education (IJAI), 2(2), 24. https://doi.org/10.3991/IJAI.V2I2.16759.
Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, https://doi.org/10.1016/J.TECHSOC.2020.101410.
Li, C., Zhang, Y., Niu, X., Chen, F., & Zhou, H. (2023). Does Artificial Intelligence promote or inhibit On-the-job learning? Human reactions to AI. Work Systems, 11(3), 114. https://doi.org/10.3390/systems11030114.
Martins, R. M., von Wangenheim, C. G., Rauber, M. F., & Hauck, J. C. (2023). Machine learning for all!—Introducing machine learning in Middle and High School. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-022-00325-y.
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241.
Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27, 7893–7925. https://doi.org/10.1007/s10639-022-10925-9.
Romero Martínez, S. J., Ordóñez Camacho, X. G., Guillén-Gamez, F. D., & Agapito, B., J (2020). Attitudes toward Technology among Distance Education students: Validation of an explanatory model. Online Learning, 24(2), 59–75. https://doi.org/10.24059/olj.v24i2.2028.
Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022a). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3, 100072. https://doi.org/10.1016/J.CAEO.2021.100072.
Sanusi, I. T., Oyelere, S. S., Vartiainen, H., Suhonen, J., & Tukiainen, M. (2022b). A systematic review of teaching and learning machine learning in K-12 education. Education and Information Technologies, 1–31. https://doi.org/10.1007/S10639-022-11416-7/TABLES/5.
Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the attitude towards Artificial Intelligence: Introduction of a short measure in german, chinese, and English Language. KI - Kunstliche Intelligenz, 35(1), 109–118. https://doi.org/10.1007/S13218-020-00689-0.
Steinfeldt, C., & Mihaljević, H. (2023). A machine learning approach to quantify gender bias in collaboration practices of mathematicians. Frontiers in Big Data, 5, 137. https://doi.org/10.3389/FDATA.2022.989469/BIBTEX.
Takıl, N., Erden, N. K., & Sarı, A. B. (2022). Investigating artificial intelligence anxiety levels of candidates in different occupational groups. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(48), 343–353. https://doi.org/10.31795/baunsobed.1165386.
Tedre, M., Vartiainen, H., Kahila, J., Toivonen, T., Jormanainen, I., & Valtonen, T. (2020). Machine learning introduces New Perspectives to Data Agency in K – 12 Computing Education. Proceedings - Frontiers in Education Conference FIE, 2020-October, https://doi.org/10.1109/FIE44824.2020.9274138.
Terzi, R. (2020). An adaptation of Artificial Intelligence anxiety scale into turkish: Reliability and validity study. International Online Journal of Education and Teaching, 7(4), 1501–1515.
Vartiainen, H., Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., & Valtonen, T. (2021). Machine learning for middle schoolers: Learning through data-driven design. International Journal of Child-Computer Interaction, 29, 100281. https://doi.org/10.1016/J.IJCCI.2021.100281.
Vasiljeva, T., Kreituss, I., & Lulle, I. (2021). Artificial Intelligence: The attitude of the public and representatives of various Industries. Journal of Risk and Financial Management, 14(8), 339. https://doi.org/10.3390/JRFM14080339.
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. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: A perspective of AI anxiety. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2022.2153147.
Yang, S., Bai, H., Abuselidze, G., & Mamaladze, L. (2021). The impact of artificial intelligence on employment before and during pandemic: A comparative analysis. Journal of Physics: Conference Series, 1840(1), 012040. https://doi.org/10.1088/1742-6596/1840/1/012040.
Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3), https://doi.org/10.1111/soc4.12962.
Zheng, L., Zhong, L., Niu, J., Long, M., & Zhao, J. (2021). Effects of personalized intervention on collaborative knowledge building, Group Performance, Socially Shared Metacognitive Regulation, and cognitive load in computer-supported collaborative learning. Educational Technology & Society, 24(3), 174–193.
Zhou, C. (2023). Integration of modern technologies in higher education on the example of artificial intelligence use. Education and Information Technologies, 28(4), 3893–3910. https://doi.org/10.1007/s10639-022-11309-9.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
The authors have no conflicts of interest to declare that are relevant to the content of this article.
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
The authors have no financial or proprietary interests in any material discussed in this article.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hopcan, S., Türkmen, G. & Polat, E. Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Educ Inf Technol 29, 7281–7301 (2024). https://doi.org/10.1007/s10639-023-12086-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-023-12086-9