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Exploring Iranian english as a foreign language teachers’ acceptance of ChatGPT in english language teaching: Extending the technology acceptance model

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Abstract

This study explores the factors influencing the acceptance of ChatGPT, an artificial intelligence chatbot, for English Language Teaching (ELT) among Iranian EFL (English as a Foreign Language) teachers. The research framework is grounded in the Technology Acceptance Model (TAM), augmented with external factors pertaining to system characteristics and individual factors. A survey questionnaire was administered to 234 Iranian EFL teachers to collect data for analysis. Quantitative methods were employed to analyze the gathered data. The findings substantiated 13 of the 14 hypothesized relationships, unveiling significant associations among multiple variables. These relationships encompassed perceived ease of use (PEOU) and perceived usefulness (PU), PEOU and behavioral intention to use (BI), PU and BI, perceived system quality (PSQ) and PU, PSQ and PEOU, online course design (OCD) and PU, PSQ and PEOU, perceived enjoyment (PE) and PEOU, PE and PU, PE and BI, perceived self-efficacy (PSE) and PU, PSE and PEOU, and subjective norm (SN) and PU, SN and PEOU. However, no statistically significant correlation emerged between OCD and PEOU. The implications of these findings are discussed, and suggestions for future research are presented.

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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.

    Article  Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2), 1–23.

    Google Scholar 

  • Al-Emran, M., & Teo, T. (2020). Do knowledge acquisition and knowledge sharing really affect e-learning adoption? An empirical study. Education and Information Technologies, 25(3), 1983–1998.

    Article  Google Scholar 

  • AlAfnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2023). Chatgpt as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology, 3(2), 60–68.

    Google Scholar 

  • Almaiah, M. A., & Alismaiel, O. A. (2018). Examination of factors influencing the use of mobile learning system: An empirical study. Education and Information Technologies, 24(1), 885–909.

    Article  Google Scholar 

  • Almaiah, M. A., Jalil, M. A., & Man, M. (2016). Extending the TAM to examine the effects of quality features on mobile learning acceptance. Journal of Computers in Education, 3(4), 453–485.

    Article  Google Scholar 

  • Alturki, U., & Aldraiweesh, A. (2022). Adoption of Google Meet by postgraduate students: The role of task technology fit and the TAM model. Sustainability, 14(23), 15765.

    Article  Google Scholar 

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

    Article  Google Scholar 

  • Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147.

    Article  Google Scholar 

  • Binyamin, S., Rutter, M., & Smith, S. (2017). Factors influencing the students’ use of learning management systems: A case study of King Abdulaziz University. In Proceedings of the 12th International Conference on e-Learning (ICEL2017), 289–297. Orlando, FL: Academic Conferences International Limited.

  • Cakır, R., & Solak, E. (2015). Attitude of Turkish EFL learners towards e-learning through TAM model. Procedia - Social and Behavioral Sciences, 176(C), 596–601.

    Article  Google Scholar 

  • Calisir, F., Altin Gumussoy, C., Bayraktaroglu, A. E., & Karaali, D. (2014). Predicting the intention to use a web-based learning system: Perceived content quality, anxiety, perceived system quality, image, and the technology acceptance model. Human Factors and Ergonomics in Manufacturing & Service Industries, 24(5), 515–531.

    Article  Google Scholar 

  • Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128–143.

    Article  Google Scholar 

  • Chen, K. Y., & Chang, M. L. (2013). User acceptance of ‘near field communication’ mobile phone service: An investigation based on the ‘unified theory of acceptance and use of technology’ model. The Service Industries Journal, 33(6), 609–623.

    Article  Google Scholar 

  • Cheng, Y. M. (2011). Antecedents and consequences of E-learning acceptance. Information Systems Journal, 21(3), 269–299.

    Article  MathSciNet  Google Scholar 

  • Cheng, Y. M. (2012). Effects of quality antecedents on e-learning acceptance. Internet Research, 22(3), 361–390.

    Article  Google Scholar 

  • Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS quarterly, vii-xvi.

  • Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211.

    Article  Google Scholar 

  • Davari, A., & Rezazadeh, A. (2013). Structural equation modeling with PLS. Tehran: Jahad University, 215(2), 224. (In Persian).

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Article  Google Scholar 

  • Esteban-Millat, I., Martínez-López, F. J., Pujol-Jover, M., Gázquez-Abad, J. C., & Alegret, A. (2018). An extension of the technology acceptance model for online learning environments. Interactive Learning Environments, 26(7), 895–910.

    Article  Google Scholar 

  • Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching, 11(2), 210–232.

    Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior. An introduction to theory and research. Addison-Wesley.

  • Fishbein, M., & Ajzen, I. (2011). Predicting and changing behavior: The reasoned action approach. Taylor & Francis.

  • Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variable and measurement error. Journal of Marketing Research, 18, 39–50.

    Article  Google Scholar 

  • Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023). How close is ChatGPT to human experts? Comparison corpus, evaluation, and detection. arXiv. arXiv:2301.07597.

  • Hancı-Azizoğlu, E. B., & Ulutaş, N. K. (2021). Creative digital writing: A multilingual perspective. Digital pedagogies and the transformation of language education, 250–266. Hershey PA, IGI Global.

  • Heath, M., Asim, S., Milman, N., & Henderson, J. (2022). Confronting tools of the oppressor: Framing just technology integration in educational technology and teacher education. Contemporary Issues in Technology and Teacher Education, 22(4), 754–777.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.

    Article  Google Scholar 

  • Hong, X., Zhang, M., & Liu, Q. (2021). Preschool teachers’ technology acceptance during the COVID19: An adapted technology acceptance model. Frontiers in Psychology, 12, 691492–691492.

    Article  Google Scholar 

  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

    Article  Google Scholar 

  • Jones, M. G., Farquhar, J. D., & Surry, D. W. (1995). Using metacognitive theories to design user interfaces for computer-based learning. Educational Technology, 35(4), 12–22.

    Google Scholar 

  • Kanwal, F., & Rehman, M. (2017). Factors affecting e-learning adoption in developing countries–empirical evidence from Pakistan’s higher education sector. Ieee Access, 5, 10968–10978.

    Article  Google Scholar 

  • Khong, H., Celik, I., Le, T. T., Lai, V. T. T., Nguyen, A., & Bui, H. (2023). Examining teachers’ behavioural intention for online teaching after COVID-19 pandemic: A large-scale survey. Education and Information Technologies, 28(5), 5999–6026.

    Article  Google Scholar 

  • Kim, G., & Lee, S. (2016). Korean students’ intentions to use mobile-assisted language learning: Applying the technology acceptance model. International Journal of Contents, 12(3), 47–53.

    Article  Google Scholar 

  • Liebrenz, M., Schleifer, R., Buadze, A., Bhugra, D., & Smith, A. (2023). Generating scholarly content with ChatGPT: Ethical challenges for medical publishing. The Lancet Digital Health, 5(3), e105–e106.

    Article  Google Scholar 

  • Liu, Y., Han, S., & Li, H. (2010). Understanding the factors driving m-learning adoption: A literature review. Campus-Wide Information Systems, 27(4), 210–226.

    Article  Google Scholar 

  • Mahmodi, M. (2017). The analysis of the factors affecting the acceptance of E-learning in higher education. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 8(1), 1–9.

    Article  Google Scholar 

  • Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: Theoretical bases and empirical validation. In proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. IEEE, Maui, HI, USA.

  • Martin-Michiellot, S., & Mendelsohn, P. (2000). Cognitive load while learning with a graphical computer interface. Journal of Computer Assisted Learning, 16(4), 284–293.

    Article  Google Scholar 

  • Mashhadi, A., Hussein, M. A., & Fahad, A. K. (2023a). Mobile learning for teacher professional development: An empirical assessment of an extended technology acceptance model. Porta Linguarum Revista Interuniversitaria De Didáctica De las Lenguas Extranjeras, 349–369.

  • Mashhadi, A., Kassim Kadhum, A., & Gooniband Shooshtari, Z. (2023b). Exploring technological pedagogical content knowledge among Iraqi high school English teachers: A comparative study during the COVID-19 pandemic. Iranian Journal of Applied Language Studies, 15(1), 141–154.

    Google Scholar 

  • McCarthy, J., Minsky, M., Sloman, A., & Gong, L. (2002). An architecture of diversity for commonsense reasoning. IBM Systems Journal, 41(3), 530–539.

    Article  Google Scholar 

  • Mendoza, G. A. G., Jung, I., & Kobayashi, S. (2017). A review of empirical studies on MOOC adoption: Applying the unified theory of acceptance and use of technology. International Journal for Educational Media and Technology, 11(1), 15–24.

    Google Scholar 

  • Mhlanga, D. (2023). Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. Retrieved from https://ssrn.com/abstract=4354422.

  • Mizumoto, A., & Eguchi, M. (2023). Exploring the potential of using an AI language model for automated essay scoring. Research Methods in Applied Linguistics, 2(2), 100050.

    Article  Google Scholar 

  • Momenanzadeh, M., Mashhadi, A., Gooniband Shooshtari, Z., & Arus-Hita, J. (2023). English as a foreign language preservice teachers’ technological pedagogical content knowledge: A quantitative comparative study. Journal of Research in Applied Linguistics, 14(2), 161–172.

    Google Scholar 

  • Monjezi, M., Mashhadi, A., & Maniati, M. (2021). COVID-19: Is it time you made the CALL. Computer Assisted Language Learning Electronic Journal, 22(2), 56–72.

    Google Scholar 

  • Ndibalema, P. (2022). Constraints of transition to online distance learning in higher education institutions during COVID-19 in developing countries: A systematic review. E-Learning and Digital Media, 19(6), 595–618.

    Article  Google Scholar 

  • Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: Investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56–73.

    Article  Google Scholar 

  • Nunnally, J. C. (1978). Psychometric Theory: 2d Ed. McGraw-Hill.

  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioral intention to use e-learning. Journal of Educational Technology & Society, 12(3), 150–162.

    Google Scholar 

  • Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17, 236–263.

    Article  Google Scholar 

  • Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning and Teaching, 6(1), 1–22.

    Google Scholar 

  • Salloum, S. A., Alhamad, A. Q., Al-Emran, M., Monem, A. A., & Shaalan, K. (2019). Exploring students’ acceptance of e-learning through the development of a comprehensive technology acceptance model. Ieee Access : Practical Innovations, Open Solutions, 7(9), 128445–128462.

    Article  Google Scholar 

  • Sánchez, R., & Hueros, A. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26(6), 1632–1640.

    Article  Google Scholar 

  • Sánchez-Prieto, J., Olmos-Migueláñez, S., & García-Peñalvo, F. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528.

    Article  Google Scholar 

  • Song, Y., & Kong, S. (2017). Investigating students’ acceptance of a statistics learning platform using technology acceptance model. Journal of Educational Computing Research, 55(6), 865–897.

    Article  Google Scholar 

  • Tao, D., Fu, P., Wang, Y., Zhang, T., & Qu, X. (2022). Key characteristics in designing massive open online courses (MOOCs) for user acceptance: An application of the extended technology acceptance model. Interactive Learning Environments, 30(5), 882–895.

    Article  Google Scholar 

  • Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers’ attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89–109.

    Article  Google Scholar 

  • Topsakal, O., & Topsakal, E. (2022). Framework for a foreign language teaching software for children utilizing AR, voicebots and ChatGPT (large language models). The Journal of Cognitive Systems, 7(2), 33–38.

    Article  MathSciNet  Google Scholar 

  • Tran, H. T. T., Nguyen, N. T., & Tang, T. T. (2023). Influences of subjective norms on teachers’ intention to use social media in working. Contemporary Educational Technology, 15(1), ep400.

    Article  Google Scholar 

  • Traxler, J., Barcena, E., Andujar, A., Jalilifar, A., & Mashhadi, A. (2023). Introduction: Teaching languages in times of social and technological change and divide. Journal of Research in Applied Linguistics, 14(2), 3–6.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). ChatGPT: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics Science and Technology Education, 19(7), 1–18.

    Article  Google Scholar 

  • Warshaw, P. R., & Davis, F. D. (1985). Disentangling behavioral intention and behavioral expectation. Journal of Experimental Social Psychology, 21, 213–228.

    Article  Google Scholar 

  • Whalen, J., & Mouza, C. (2023). ChatGPT: Challenges, opportunities, and implications for teacher education. Contemporary Issues in Technology and Teacher Education, 23(1), 1–23.

    Google Scholar 

  • Wiggins, G. P. (1998). Educative assessment: Designing assessments to inform and improve student performance. Jossey-Bass.

  • Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.

    Article  Google Scholar 

  • Yang, M., Shao, Z., Liu, Q., & Liu, C. (2017). Understanding the quality factors that influence the continuance intention of students toward participation in MOOCs. Educational Technology Research and Development, 65, 1195–1214.

    Article  Google Scholar 

  • Yee-Loong Chong, A., Ooi, K. B., Lin, B., & Tan, B. I. (2010). Online banking adoption: An empirical analysis. International Journal of bank Marketing, 28(4), 267–287.

    Article  Google Scholar 

  • Zainab, B., Awais Bhatti, M., & Alshagawi, M. (2017). Factors affecting e-training adoption: An examination of perceived cost, computer self-efficacy and the technology acceptance model. Behaviour & Information Technology, 36(12), 1261–1273.

    Article  Google Scholar 

  • Zhou, L., Xue, S., & Li, R. (2022). Extending the technology acceptance model to explore students’ intention to use an online education platform at a university in China. Sage Open, 12(1), 1–13.

    Article  Google Scholar 

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  1. Tehran North Branch, Islamic Azad University, Tehran, Iran

    Hoora Dehghani

  2. Department of English Language and Literature, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Amir Mashhadi

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  1. Hoora Dehghani
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Dehghani, H., Mashhadi, A. Exploring Iranian english as a foreign language teachers’ acceptance of ChatGPT in english language teaching: Extending the technology acceptance model. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12660-9

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