Correlation between artificial intelligence in education and teacher self-efficacy beliefs: a review

Yıl 2023, Sayı: 34, 1354 - 1365, 22.06.2023

Bedirhan Berk Oran

https://doi.org/10.29000/rumelide.1316378

Cited By: 5

Öz

Teachers' self-efficacy (TSE) beliefs affect not just their performance and motivation, but also the academic success of their pupils. As a result, researchers focused on the crucial self-beliefs of educators and its origins. This review article seeks to fill a gap regarding the correlation of teacher self-efficacy and its relation with the recently developed and ever-growing artificial intelligence (AI) technology by combining literature reviews from both of the topics to get a whole understanding and provide a solid framework for the suggestions of this paper. In addition, examples of how AI can be used in the classroom and the skills required for this use are mentioned and linked to educators' self-efficacy beliefs. Findings of literature review suggested that teacher self-efficacy is affected by various factors, but no studies which measured the correlation between AI literacy and teacher self-efficacy beliefs were found, thus, it is suggested that future research should be conducted to measure the correlation between AI literacy and teacher self-efficacy beliefs. Based on studies on these main topics that were reviewed separately, following implications were reached: (1) identifying ways that educators can take AI's advantage to improve their work efficiency rather than just relying on the tool to get the job done is crucial, (2) Policymakers should set up both pre-service and in-service training programs because they might offer novice teachers and seasoned educators the chance to learn through observational learning to increase their teacher self-efficacy regarding the use of AI in education.

Anahtar Kelimeler

teacher self-efficacy, artificial intelligence (AI), AI in education, ChatGPT, educational technologies

Kaynakça

• Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. https://digitalcommons.uri.edu/cba_facpubs/548
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215.
• Bandura, A., (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall, Englewood Cliffs, NJ.
• Bandura, A., (1997). Self-efficacy: The Exercise of Control. Freeman, New York.
• Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016). OpenAI Gym. arXiv. https://doi.org/10.48550/arXiv.1606.01540
• Budzianowski, P., & Vulić, I. (2019). Hello, it's GPT-2--how can I help you? towards the use of pretrained language models for task-oriented dialogue systems. arXiv. https://doi.org/10.48550/arXiv.1907.05774
• Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270–295. https://doi.org/10.1006/ceps.2001.1094
• Cassidy, C. (2023). Australian universities to return to ‘pen and paper’ exams after students caught using AI to write essays. The Guardian Online. Available at https://www.theguardian.com/australia-news/2023/jan/10/universities-to-return-to-pen-and-paper-exams-after-students-caught-using-ai-to-write-essays, accessed 11/05/23.
• Choi, Jonathan H. & Hickman, Kristin E. & Monahan, Amy & Schwarcz, Daniel B., ChatGPT Goes to Law School (January 23, 2023). Minnesota Legal Studies Research Paper No. 23-03, http://dx.doi.org/10.2139/ssrn.4335905
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
• Coppin, B. (2004). Artificial Intelligence Illuminated. Boston, MA, USA: Jones and Bartlett
• Cote, T., & Milliner, B. (2018). A Survey of EFL Teachers' Digital Literacy: A Report from a Japanese University. Teaching English with technology, 18, 71-89.
• Dale, R. (2017). NLP in a post-truth world. Natural Language Engineering, 23(2), 319-324.
• Dale, R. (2021). GPT-3 What’s it good for? Natural Language Engineering, 27(1), 113-118.
• Davies H. C., Eynon R. and Salveson C. (2020), “The mobilization of AI in education: a Bourdieusean field analysis”, Sociology, available at https://doi. org/10.1177/0038038520967888, accessed 23 June 2022.
• Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv. https://doi.org/10.48550/arXiv.1810.04805
• Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., & Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
• Elsen-Rooney M. (2023). NYC education department blocks ChatGPT on school devices, networks. Chalkbeat New York. Available at: https://ny.chalkbeat.org/2023/1/3/23537987/nyc-schools-ban-chatgpt-writing-artificial-intelligence, accessed 11/05/23.
• Erhan, D., Bengio, Y., Courville, A., Manzagol, P., & Vincent, P. (2010). Why does unsupervised pre-training help deep learning. Journal of Machine Learning Research, 11, 625-660.
• Frye, B. (2022). Should using an AI text generator to produce academic writing be plagiarism? SSRN 2022; published online Dec 20. https://ssrn.com/abstract=4292283 (preprint).
• Gómez-Trigueros, I. M., Ruiz-Bañuls, M., & Ortega-Sánchez, D. (2019). Digital Literacy of Teachers in Training: Moving from ICTs (Information and Communication Technologies) to LKTs (Learning and Knowledge Technologies). Education Sciences, 9(4), 274. https://doi.org/10.3390/educsci9040274
• Gordijn, B. and Have, H. T. (2023). “ChatGPT: evolution or revolution?”, Medicine, Health Care and Philosophy, Vol. 26 No. 1, pp. 1-2.
• Hao, K. (2019). China has started a grand experiment in AI education. It could reshape how the world learns. MIT Technology Review. Retrieved from https://www.technologyreview.com/2019/08/02/131198/china-squirrelhas-started-a-grand-experiment-in-ai-education-it-could-reshape-how-the/
• Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
• Henson, R.K. (2002). From adolescent angst to adulthood: Substantive implications and measurement dilemmas in the development of teacher efficacy research. Educational Psychologist 37, no. 3: 137-150.
• Kasalak, G., Dagyar, M., (2020). The relationship between teacher self-efficacy and teacher job satisfaction: A meta-analysis of the teaching and learning international survey (TALIS). Educational Sciences: Theory and Practice, vol.20, no.3, 16-33.
• Kay, Judy. (2012). AI and Education: Grand Challenges. Intelligent Systems, IEEE. 27. 66-69. 10.1109/MIS.2012.92.
• Klassen, R. M., and Durksen, T. L. (2014). Weekly self-efficacy and work stress during the teaching practicum: a mixed methods study. Learn. Instr. 33, 158–169. doi: 10.1016/j.learninstruc.2014.05.003
• Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for Language Teaching and Learning. RELC Journal, 0(0). https://doi.org/10.1177/00336882231162868
• Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2021). GPT understands, too. arXiv. https://doi.org/10.48550/arXiv.2103.10385
• Lucy, L., & Bamman, D. (2021). Gender and representation bias in GPT-3 generated stories. Proceedings of the Workshop on Narrative Understanding, 3, 48-55.
• Lund, Brady & Wang, Ting. (2023). Chatting about ChatGPT: How may AI and GPT impact academia and libraries? Library Hi Tech News. 40. 10.1108/LHTN-01-2023-0009.
• Luszczynska, A., Schwarzer, R., (2005). Social cognitive theory. In: Conner, M., Norman, P. (Eds.), Predicting Health Behaviour: Research and Practice with Social Cognition Models, second ed. Open University Press, Maidenhead, UK, pp. 127–169.
• MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022). Generating diverse code explanations using the GPT-3 large language model. Proceedings of the ACM Conference on International Computing Education Research, 2, 37-39.
• Manyika, J. (2011). Big data: The next frontier for innovation, competition, and productivity.
• Marcus, G., Davis, E., & Aaronson, S. (2022). A very preliminary analysis of DALL-E 2. ArXiv pre-print. Retrieved from https://doi.org/10.48550/arXiv.2204.13807
• Maslach, C., & Jackson, S. E. (1981). The measurement of experienced burnout. Journal of organizational behavior, 2(2), 99-113.
• Miao, F. & Holmes, W. (2021). AI and education: guidance for policy-makers, UNESCO.
• Mollman, S. (2022). ChatGPT gained 1 million users in under a week. Retrieved from https://www.yahoo.com/lifestyle/chatgpt-gained-1-million-followers
• Moorhouse, B.L. (2023) Teachers’ digital technology use after a period of online teaching. ELT Journal. Epub online. DOI: 10.1093/elt/ccac050
• Nabavi, R. T. (2012). Bandura’s Social learning theory and social cognitive learning theory. Retrieved on 16/02/2021 from https://www.researchgate.net/profile/Razieh- TadayonNabavi/publication/267750204_Bandura%27s_Social_Learning_Theory_Social_Cognitive_Learning_Theory/links/545914d90cf26d5090ad007b/Banduras-SocialLearning-Theory-Social-CognitiveLearning-Theory.pdf
• 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
• OECD (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots, OECD Publishing, Paris, https://doi.org/10.1787/589b283f-en.
• Pajares, F. (2002). Overview of social cognitive theory and of selfefficacy. Retrieved 08/05/23 from http://people.wku.edu/richard.miller/banduratheory.pdf
• Pajares, F., & Valiante, G. (2006). Self-Efficacy Beliefs and Motivation in Writing Development. In C. A. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (pp. 158–170). The Guilford Press.
• Pfitzner-Eden, F. (2016). Why do I feel more confident? Bandura’s sources predict preservice teachers’ latent changes in teacher self-efficacy. Frontiers. https://www.frontiersin.org/articles/10.3389/fpsyg.2016.01486/full
• Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13.
• Qasem, F. (2023). "ChatGPT in scientific and academic research: future fears and reassurances", Library Hi Tech News, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/LHTN-03-2023-0043
• Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. Retrieved from https://www.cs.ubc.ca/~amuham01/LING530/papers/radford2018improving.pdf
• Schunk, D. H. (1984). Self-efficacy perspective on achievement behavior. Educational Psychologist, 19, 48-58.
• Seldon A. & Abidoye O. (2018), The fourth education revolution: will artificial intelligence liberate or infantilise humanity? The University of Buckingham Press, London.
• Stajkovic, Alex & Luthans, Fred. (2002). Social cognitive theory and self-efficacy: Implications for motivation theory and practice.. In R.M. Steers, L.W. Porter, & G.A., Bigley (Eds.), Motivation and Work Behavior (7th ed.). 126-140.
• Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049. https://doi.org/10.1016/j.caeai.2022.100049
• Su, J., Zhong, Y., & Ng, D. T. K. (2022). A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3, 100065. https://doi.org/10.1016/j.caeai.2022.100065
• Topping, K., (2005). Trends in peer learning. Educational Psychology 25 (6), 631–645.
• Wang, S., Scells, H., Koopman, B. and Zuccon, G. (2023). “Can ChatGPT write a good Boolean query for systematic review literature search?”, ArXiv Preprint ArXiv:2302.03495.
• Whitby, B. (2008). Artificial Intelligence: A Beginner’s Guide. Oxford, U.K.: Oneworld.
• Woolfolk, A. E., & Hoy, W. K. (1990). Prospective teachers' sense of efficacy and beliefs about control. Journal of Educational Psychology, 82(1), 81–91. https://doi.org/10.1037/0022-0663.82.1.81
• Zhou, X., Chen, Z., Jin, X., & Wang, W. Y. (2021). HULK: An energy efficiency benchmark platform for responsible natural language processing. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, 16, 329-336.