Factors Influencing Learner Attitudes Towards ChatGPT-Assisted Language Learning in Higher Education

References

• Al-Emran, M., Arpaci, I., & Salloum, S. A. (2020). An empirical examination of continuous intention to use m-learning: An integrated model. Education and Information Technologies, 25(4), 2899–2918. https://doi.org/10.1007/s10639-019-10094-2
   Web of Science ®Google Scholar
• Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102(1), 67–86. https://doi.org/10.1016/j.chb.2019.08.004
   Web of Science ®Google Scholar
• Almahri, F. A. J., Bell, D., & Merhi, M. (2020, March). Understanding student acceptance and use of chatbots in the United Kingdom universities: A structural equation modelling approach. In 2020 6th International Conference on Information Management (ICIM) (pp. 284–288). IEEE. https://doi.org/10.1109/ICIM49319.2020.244712
   Google Scholar
• Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on students’ intention to use E-learning systems. IEEE Access, 7, 26797–26809. https://doi.org/10.1109/ACCESS.2019.2899368
   Web of Science ®Google Scholar
• Al-Sharafi, M. A., Al-Emran, M., Iranmanesh, M., Al-Qaysi, N., Iahad, N. A., & Arpaci, I. (2022). Understanding the impact of knowledge management factors on the sustainable use of AI-based chatbots for educational purposes using a hybrid SEM-ANN approach. Interactive Learning Environments, 30, 1–20. https://doi.org/10.1080/10494820.2022.2075014
   Web of Science ®Google Scholar
• Asante, I. O., Jiang, Y., Hossin, A. M., & Luo, X. (2023). Optimization of consumer engagement with artificial intelligence elements on electronic commerce platforms. Journal of Electronic Commerce Research, 24(1), 7–28.
   Web of Science ®Google Scholar
• Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020). I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics, 54, 101473. https://doi.org/10.1016/j.tele.2020.101473
   Web of Science ®Google Scholar
• Balakrishnan, J., Abed, S. S., & Jones, P. (2022). The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services? Technological Forecasting and Social Change, 180, 121692. https://doi.org/10.1016/j.techfore.2022.121692
   Web of Science ®Google Scholar
• Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10, 1652. https://doi.org/10.3389/fpsyg.2019.01652
   PubMed Web of Science ®Google Scholar
• Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers, 25(1), 161–182. https://doi.org/10.1007/s10796-022-10291-4
   Web of Science ®Google Scholar
• Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2023.2172044
   Web of Science ®Google Scholar
• DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
   Web of Science ®Google Scholar
• Deng, X., & Yu, Z. (2023). An extended hedonic motivation adoption model of TikTok in higher education. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-023-11749-x
   Web of Science ®Google Scholar
• Ebadi, S., & Amini, A. (2022). Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: A case of CSIEC chatbot. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2022.2096638
   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/10.2307/3151312
   Web of Science ®Google Scholar
• Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use ChatGPT for educational purposes: Findings from PLS-SEM and fsQCA. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2023.2226495
   Web of Science ®Google Scholar
• Grant, C., & Osanloo, A. (2014). Understanding, selecting, and integrating a theoretical framework in dissertation research: Creating the blueprint for your “house”. Administrative Issues Journal Education Practice and Research, 4(2), 12–26. https://dc.swosu.edu/aij/vol4/iss2/4 https://doi.org/10.5929/2014.4.2.9
   Google Scholar
• Harel, I., & Papert, S. (1990). Software design as a learning environment. Interactive Learning Environments, 1(1), 1–32. https://doi.org/10.1080/1049482900010102
   Google Scholar
• Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
   Google Scholar
• Hair, J. F., Hult, G. T. M., & Ringle, C. M. (2014). Partial least squares structural equation modeling (PLS-SEM). SAGE Publications.
   Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
   Web of Science ®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(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Hsu, T. C., Huang, H. L., Hwang, G. J., & Chen, M. S. (2023). Effects of incorporating an expert decision-making mechanism into chatbots on students? Achievement, enjoyment. And Anxiety. Educational Technology & Society, 26(1), 218–231. https://doi.org/10.30191/ETS.202301_26(1).0016
   Web of Science ®Google Scholar
• Huang, W., Hew, K. F., & Fryer, L. K. (2022). Chatbots for language learning – are they really useful? A systematic review of chatbot-supported language learning. Journal of Computer Assisted Learning, 38(1), 237–257. https://doi.org/10.1111/jcal.12610
   Web of Science ®Google Scholar
• Joo, Y. J., Park, S., & Shin, E. K. (2017). Students’ expectation, satisfaction, and continuance intention to use digital textbooks. Computers in Human Behavior, 69(2), 83–90. https://doi.org/10.1016/j.chb.2016.12.025
   Web of Science ®Google Scholar
• Kamila, M. K., & Jasrotia, S. S. (2023). Ethical issues in the development of artificial intelligence: Recognizing the risks. International Journal of Ethics and Systems. Advance online publication. https://doi.org/10.1108/IJOES-05-2023-0107
   Web of Science ®Google Scholar
• Kim-Soon, N., Ibrahim, M. A., Razzaly, W., Ahmad, A. R., & Sirisa, N. M. X. (2017). Mobile technology for learning satisfaction among students at Malaysian technical universities (MTUN). Advanced Science Letters, 23(1), 223–226. https://doi.org/10.1166/asl.2017.7140
   Google Scholar
• Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
   Google Scholar
• Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
   Google Scholar
• Kohnke, L. (2023). L2 learners’ perceptions of a chatbot as a potential independent language learning tool. International Journal of Mobile Learning and Organisation, 17(1/2), 214. https://doi.org/10.1504/IJMLO.2023.128339
   Google Scholar
• Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
   Web of Science ®Google Scholar
• Lee, J. G., Lee, J., & Lee, D. (2021). Cheerful encouragement or careful listening: The dynamics of robot etiquette at children’s different developmental stages. Computers in Human Behavior, 118, 106697. https://doi.org/10.1016/j.chb.2021.106697
   Web of Science ®Google Scholar
• Li, C., He, L., & Wong, I. A. (2021). Determinants predicting undergraduates’ intention to adopt e-learning for studying English in Chinese higher education context: A structural equation modelling approach. Education and Information Technologies, 26(4), 4221–4239. https://doi.org/10.1007/s10639-021-10462-x
   Web of Science ®Google Scholar
• Li, X., Xie, S., Ye, Z., Ma, S., & Yu, G. (2022). Investigating patients’ continuance intention towards conversational agents in outpatient departments: Cross-sectional field survey. Journal of Medical Internet Research, 24(11), e40681. https://doi.org/10.2196/40681
   PubMed Web of Science ®Google Scholar
• Liaw, S. S. (2007). Understanding computers and the Internet as a work-assisted tool. Computers in Human Behavior, 23(1), 399–414. https://doi.org/10.1016/j.chb.2004.10.018
   Web of Science ®Google Scholar
• Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864–873. https://doi.org/10.1016/j.compedu.2007.09.005
   Web of Science ®Google Scholar
• Liaw, S. S., & Huang, H. M. (2016). Investigating learner attitudes towards e-books as learning tools: Based on the activity theory approach. Interactive Learning Environments, 24(3), 625–643. https://doi.org/10.1080/10494820.2014.915416
   Web of Science ®Google Scholar
• Lin, Y. P., & Yu, Z. G. (2023a). A bibliometric analysis of artificial intelligence chatbots in educational contexts. Interactive Technology and Smart Education. Advance online publication. https://doi.org/10.1108/ITSE-12-2022-0165
   Web of Science ®Google Scholar
• Lin, Y. P., & Yu, Z. G. (2023b). Extending technology acceptance model to higher-education students’ use of digital academic reading tools on computers. International Journal of Educational Technology in Higher Education. Advance online publication. 20(1), 34. https://doi.org/10.1186/s41239-023-00403-8
   Web of Science ®Google Scholar
• Ma, L., & Lee, C. S. (2019). Investigating the adoption of MOOCs: A technology user-environment perspective. Journal of Computer Assisted Learning, 35(1), 89–98. https://doi.org/10.1111/jcal.12314
   Web of Science ®Google Scholar
• Malik, R., Shrama, A., Trivedi, S., & Mishra, R. (2021). Adoption of chatbots for learning among university students: Role of perceived convenience and enhanced performance. International Journal of Emerging Technologies in Learning (iJET), 16(18), 200–212. https://www.learntechlib.org/p/220124/ https://doi.org/10.3991/ijet.v16i18.24315
   Web of Science ®Google Scholar
• Muñoz-Carril, P. C., Hernández-Sellés, N., Fuentes-Abeledo, E. J., & González-Sanmamed, M. (2021). Factors influencing students’ perceived impact of learning and satisfaction in Computer Supported Collaborative Learning. Computers & Education, 174, 104310. https://doi.org/10.1016/j.compedu.2021.104310
   Web of Science ®Google Scholar
• Pask, G. (1976). Conversation theory. Applications in education and epistemology. Elsevier.
   Google Scholar
• Pavlik, J. V. (2023). Collaborating with ChatGPT: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), 84–93. https://doi.org/10.1177/10776958221149577
   Google Scholar
• Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199–3226. https://doi.org/10.1108/IJCHM-04-2020-0259
   Web of Science ®Google Scholar
• Pozón-López, I., Higueras-Castillo, E., Muñoz-Leiva, F., & Liébana-Cabanillas, F. J. (2021). Perceived user satisfaction and intention to use massive open online courses (MOOCs). Journal of Computing in Higher Education, 33(1), 85–120. https://doi.org/10.1007/s12528-020-09257-9
   Web of Science ®Google Scholar
• Rospigliosi, P. A. (2023). Artificial intelligence in teaching and learning: What questions should we ask of ChatGPT? Interactive Learning Environments, 31(1), 1–3. https://doi.org/10.1080/10494820.2023.2180191
   Web of Science ®Google Scholar
• Salloum, S. A., Alhamad, A. Q. M., 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, 7, 128445–128462. https://doi.org/10.1109/ACCESS.2019.2939467
   Web of Science ®Google Scholar
• Sayaf, A. M., Alamri, M. M., Alqahtani, M. A., & Al-Rahmi, W. M. (2021). Information and communications technology used in higher education: An empirical study on digital learning as sustainability. Sustainability, 13(13), 7074. https://doi.org/10.3390/su13137074
   Web of Science ®Google Scholar
• Sharples, M., Taylor, J., & Vavoula, G. (2005). Towards a theory of mobile learning. Proceedings of MLearn 2005 conference 25–28 October, South Africa.
   Google Scholar
• Sparks, R., & Alamer, A. (2022). Long-term impacts of L1 language skills on L2 anxiety: The mediating role of language aptitude and L2 achievement. Language Teaching Research. Advance online publication. https://doi.org/10.1177/13621688221104392
   Web of Science ®Google Scholar
• Stewart, A., Thrasher, A., Goldberg, J., & Shea, J. (2012). A framework for understanding modifications to measures for diverse populations. Journal of Aging and Health, 24(6), 992–1017. https://doi.org/10.1177/0898264312440321
   PubMed Web of Science ®Google Scholar
• Svikhnushina, E., & Pu, P. (2022). PEACE: A model of key social and emotional qualities of conversational chatbots. ACM Transactions on Interactive Intelligent Systems, 12(4), 1–29. https://doi.org/10.1145/3531064
   Google Scholar
• Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 1–24. https://doi.org/10.1186/s40561-023-00237-x
   Google Scholar
• Tseng, T. H., Lin, S., Wang, Y.-S., & Liu, H.-X. (2019). Investigating teachers’ adoption of MOOCs: The perspective of UTAUT2. Interactive Learning Environments, 30(4), 635–650. https://doi.org/10.1080/10494820.2019.1674888
   Web of Science ®Google Scholar
• Wu, R., & Yu, Z. (2023). Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology. Advance online publication. https://doi.org/10.1111/bjet.13334
   PubMed Web of Science ®Google Scholar
• Wut, T. M., & Lee, S. W. (2022). Factors affecting students’ online behavioral intention in using discussion forum. Interactive Technology and Smart Education, 19(3), 300–318. https://doi.org/10.1108/ITSE-02-2021-0034
   Web of Science ®Google Scholar
• Xia, Q., Chiu, T. K. F., Chai, C. S., & Xie, K. (2023). The mediating effects of needs satisfaction on the relationships between prior knowledge and self‐regulated learning through artificial intelligence chatbot. British Journal of Educational Technology, 54(4), 967–986. https://doi.org/10.1111/bjet.13305
   Web of Science ®Google Scholar
• Yin, J., Goh, T. T., Yang, B., & Xiaobin, Y. (2021). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154–177. https://doi.org/10.1177/0735633120952067
   Web of Science ®Google Scholar
• Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2
   Web of Science ®Google Scholar
• Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group.
   Google Scholar