Résumés
Abstract
Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access to AI applications. However, little is written about the factors that influence their intention to use AI in distributed EFL learning contexts. This mixed-methods study, based on the technology acceptance model (TAM), examined the determinants of behavioral intention to use AI among 464 Chinese EFL college learners. As to quantitative data, a structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed that perceived ease of use significantly and positively predicts perceived usefulness and attitude toward AI. Second, attitude toward AI significantly and positively predicts behavioral intention to use AI. However, contrary to the TAM assumptions, perceived usefulness does not significantly predict either attitude toward AI or behavioral intention to use AI. Third, mediation analyses suggest that perceived ease of use has a significant and positive impact on students’ behavioral intention to use AI through their attitude toward AI, rather than through perceived usefulness. As to qualitative data, semi-structured interviews with 15 learners, analyzed by the software MAXQDA 2022, provide a nuanced understanding of the statistical patterns. This study also discusses the theoretical and pedagogical implications and suggests directions for future research.
Keywords:
- artificial intelligence,
- AI,
- EFL college learner,
- behavioral intention,
- distributed learning
Veuillez télécharger l’article en PDF pour le lire.
Télécharger
Parties annexes
Bibliography
- An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2022). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28, 5187–5208. https://doi.org/10.1007/s10639-022-11286-z
- An, X., Chai, C. S., Li, Y., Zhou, Y., & Yang, B. (2023). Modeling students’ perceptions of artificial intelligence assisted language learning. Computer Assisted Language Learning, 1–22. https://doi.org/10.1080/09588221.2023.2246519
- Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, Article 100099. https://www.doi.org/10.1016/j.caeai.2022.100099
- Ayedoun, E., Hayashi, Y., & Seta, K. (2019). Adding communicative and affective strategies to an embodied conversational agent to enhance second language learners’ willingness to communicate. International Journal of Artificial Intelligence in Education, 29(1), 29–57. https://doi.org/10.1007/s40593-018-0171-6
- Barrot, J. S. (2022). Integrating technology into ESL/EFL writing through Grammarly. RELC Journal, 53(3), 764–768. https://doi.org/10.1177/0033688220966632
- Bearman, M., Ryan, J., & Ajjawi, R. (2023). Discourses of artificial intelligence in higher education: A critical literature review. Higher Education, 86(2), 369–385. https://doi.org/10.1007/s10734-022-00937-2
- Betal, A. (2023). Enhancing second language acquisition through artificial intelligence (AI): Current insights and future directions. Journal for Research Scholars and Professionals of English Language Teaching, 7(39). https://doi.org/10.54850/jrspelt.7.39.003
- Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
- Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education, 15, Article 22. https://doi.org/10.1186/s41239-018-0109-y
- Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
- Chen, H., & Pan, J. (2022). Computer or human: A comparative study of automated evaluation scoring and instructors’ feedback on Chinese college students’ English writing. Asian-Pacific Journal of Second and Foreign Language Education, 7, Article 34. https://doi.org/10.1186/s40862-022-00171-4
- Chen, X., Xie, H., & Hwang, G.-J. (2020). A multi-perspective study on artificial intelligence in education: Grants, conferences, journals, software tools, institutions, and researchers. Computers and Education: Artificial Intelligence, 1, Article 100005. https://doi.org/10.1016/j.caeai.2020.100005
- Creswell, J. W. (2014). A concise introduction to mixed methods research. SAGE Publications.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
- Derakhshan, A., Wang, Y., Wang, Y., & Ortega-Martín, J. L. (2023). Towards innovative research approaches to investigating the role of emotional variables in promoting language teachers’ and learners’ mental health. International Journal of Mental Health Promotion, 25(7), 823–832. https://doi.org/10.32604/ijmhp.2023.029877
- Divekar, R. R., Drozdal, J., Chabot, S., Zhou, Y., Su, H., Chen, Y., Zhu, H., Hendler, J. A., & Braasch, J. (2022). Foreign language acquisition via artificial intelligence and extended reality: Design and evaluation. Computer Assisted Language Learning, 35(9), 2332–2360. https://doi.org/10.1080/09588221.2021.1879162
- Dizon, G. (2020). Evaluating intelligent personal assistants for L2 listening and speaking development. Language, Learning and Technology, 24(1), 16–26. https://doi.org/10125/44705
- 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
- Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning & Teaching, 21(1), 37–56. https://doi.org/10.1177/14757257211037149
- Gao, Q., Yan, Z., Zhao, C., Pan, Y., & Mo, L. (2014). To ban or not to ban: Differences in mobile phone policies at elementary, middle, and high schools. Computers in Human Behavior, 38, 25–32. https://doi.org/10.1016/j.chb.2014.05.011
- Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression based approach. Guilford Press.
- Hu, L.-t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
- Huang, F., & Teo, T. (2020). Influence of teacher-perceived organisational culture and school policy on Chinese teachers’ intention to use technology: An extension of technology acceptance model. Educational Technology Research and Development, 68(3), 1547–1567. https://doi.org/10.1007/s11423-019-09722-y
- Huang, H.-M., & Liaw, S.-S. (2005). Exploring users’ attitudes and intentions toward the web as a survey tool. Computers in Human Behavior, 21(5), 729–743. https://doi.org/10.1016/j.chb.2004.02.020
- Janbi, N., Katib, I., & Mehmood, R. (2023). Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture. Intelligent Systems With Applications, 18, Article 200231. https://doi.org/10.1016/j.iswa.2023.200231
- Jiang, R. (2022). How does artificial intelligence empower EFL teaching and learning nowadays? A review on artificial intelligence in the EFL context. Frontiers in Psychology, 13, Article 1049401. https://doi.org/10.3389/fpsyg.2022.1049401
- Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, Article 101925. https://doi.org/10.1016/j.tele.2022.101925
- Klotz, A. C., Swider, B. W., & Kwon, S. H. (2023). Back-translation practices in organizational research: Avoiding loss in translation. Journal of Applied Psychology, 108(5), 699–727. https://doi.org/10.1037/apl0001050
- Klimova, B., Pikhart, M., Benites, A. D., Lehr, C., & Sanchez-Stockhammer, C. (2023). Neural machine translation in foreign language teaching and learning: A systematic review. Education and Information Technologies, 28(1), 663–682. https://doi.org/10.1007/s10639-022-11194-2
- Kline, R. B. (2016). Principles and practice of structural equation modeling (6th ed.). Guilford Press.
- Kohnke, L., Moorhouse, B. L., & Zou, D. (2023a). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
- Kohnke, L., Moorhouse, B. L., & Zou, D. (2023b). Exploring generative artificial intelligence preparedness among university language instructors: A case study. Computers and Education: Artificial Intelligence, 5, Article 100156. https://doi.org/10.1016/j.caeai.2023.100156
- Kuddus, K. (2022). Artificial intelligence in language learning: Practices and prospects. In A. Mire, S. Malik, & A. K. Tyagi (Eds.), Advanced analytics and deep learning models (pp. 3–18). Wiley. https://doi.org/10.1002/9781119792437.ch1
- Li, K. (2023). Determinants of college students’ actual use of AI-based systems: An extension of the technology acceptance model. Sustainability, 15(6), Article 5221. https://www.mdpi.com/2071-1050/15/6/5221
- Liu, G., & Ma, C. (2024). Measuring EFL learners’ use of ChatGPT in informal digital learning of English based on the technology acceptance model. Innovation in Language Learning and Teaching, 18(2), 125–138. https://doi.org/10.1080/17501229.2023.2240316
- Liu, G. L., & Wang, Y. (2024). Modeling EFL teachers’ intention to integrate informal digital learning of English (IDLE) into the classroom using the theory of planned behavior. System, 120, Article 103193. https://doi.org/10.1016/j.system.2023.103193
- Muftah, M., Al-Inbari, F. A. Y., Al-Wasy, B. Q., & Mahdi, H. S. (2023). The role of automated corrective feedback in improving EFL learners’ mastery of the writing aspects. Psycholinguistics, 34(2), 82–109. https://doi.org/10.31470/2309-1797-2023-34-2-82-109
- Namaziandost, E., Hashemifardnia, A., Bilyalova, A. A., Fuster-Guillén, D., Palacios Garay, J. P., Diep, L. T. N., Ismail, H., Sundeeva, L. A., Hibana, & Rivera-Lozada, O. (2021). The effect of WeChat-based online instruction on EFL learners’ vocabulary knowledge. Education Research International, 2021, Article 8825450. https://doi.org/10.1155/2021/8825450
- Namaziandost, E., Razmi, M. H., Atabekova, A., Shoustikova, T., & Kussanova, B. H. (2021). An account of Iranian intermediate EFL learners’ vocabulary retention and recall through spaced and massed distribution instructions. Journal of Education, 203(2), 275–284. https://doi.org/10.1177/00220574211031949
- Noar, S. M. (2003). The role of structural equation modeling in scale development. Structural Equation Modeling: A Multidisciplinary Journal, 10(4), 622–647. https://doi.org/10.1207/S15328007SEM1004_8
- Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, Article 100020. https://doi.org/10.1016/j.caeai.2021.100020
- Rezai, A. (2023). Investigating the association of informal digital learning of English with EFL learners’ intercultural competence and willingness to communicate: A SEM study. BMC Psychology, 11, Article 314(2023). https://doi.org/10.1186/s40359-023-01365-2
- Schepman, A., & Rodway, P. (2022). The General Attitudes towards Artificial Intelligence Scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human-Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400
- Selwyn, N. (2016). Is technology good for education? Polity Press.
- Shortt, M., Tilak, S., Kuznetcova, I., Martens, B., & Akinkuolie, B. (2023). Gamification in mobile-assisted language learning: A systematic review of Duolingo literature from public release of 2012 to early 2020. Computer Assisted Language Learning, 36(3), 517–554. https://doi.org/10.1080/09588221.2021.1933540
- Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7(4), 422–445.
- Siyam, N. (2019). Factors impacting special education teachers’ acceptance and actual use of technology. Education and Information Technologies, 24(3), 2035–2057. https://doi.org/10.1007/s10639-018-09859-y
- Teo, T., Huang, F., & Hoi, C. K. W. (2017). Explicating the influences that explain intention to use technology among English teachers in China. Interactive Learning Environments, 26(4), 460–475. https://doi.org/10.1080/10494820.2017.1341940
- Ulla, M. B., Perales, W. F., & Busbus, S. O. (2023). ‘To generate or stop generating response’: Exploring EFL teachers’ perspectives on ChatGPT in English language teaching in Thailand. Learning: Research and Practice, 9(2), 168–182. https://doi.org/10.1080/23735082.2023.2257252
- Ursavaş, Ö. F. (2022). Technology acceptance model: History, theory, and application. In Ö. F. Ursavaş (Ed.), Conducting technology acceptance research in education: Theory, models, implementation, and analysis (pp. 57–91). Springer International Publishing. https://doi.org/10.1007/978-3-031-10846-4_4
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
- Wang, Y., Yu, L., & Yu, Z. (2022). An extended CCtalk technology acceptance model in EFL education. Education and Information Technologies, 27, 6621–6640. https://doi.org/10.1007/s10639-022-10909-9
- Wang, Y. L., Wang, Y. X., Pan, Z. W., & Ortega-Martín, J. L. (2023). The predicting role of EFL students’ achievement emotions and technological self-efficacy in their technology acceptance. The Asia-Pacific Education Researcher, 2023. https://doi.org/10.1007/s40299-023-00750-0
- Yang, H., Gao, C., & Shen, H.-z. (2023). Learner interaction with, and response to, AI-programmed automated writing evaluation feedback in EFL writing: An exploratory study. Education and Information Technologies, 29, 3837–3858. https://doi.org/10.1007/s10639-023-11991-3
- Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research and future directions. Computers and Education: Artificial Intelligence, 2, Article 100025. https://doi.org/10.1016/j.caeai.2021.100025
- Zhi, R., Wang, Y., & Wang, Y. (2023). The role of emotional intelligence and self-efficacy in EFL teachers’ technology adoption. The Asia-Pacific Education Researcher, 2023. https://doi.org/10.1007/s40299-023-00782-6