Research Article
Entangled cognition in EFL education: The role of generative AI
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1 Graduate Institute of Hospitality Management, National Kaohsiung University of Hospitality and Tourism, Kaohsiung City, TAIWAN* Corresponding Author
Contemporary Educational Technology, 17(4), October 2025, ep619, https://doi.org/10.30935/cedtech/17621
Published: 22 December 2025
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ABSTRACT
This study examines the use of generative artificial intelligence, i.e., ChatGPT, in English as a foreign language (EFL) learning, emphasizing the mediating role of entangled cognition and the effects of the learning outcomes of the tourism students. The research was designed to a quasi-experiment which included 96 participants (48 in an experimental group and 48 in a control group) who were sampled based on convenience to the Spring 2024 semester in one university in southern Taiwan. The “custom virtual language course” experimental group used ChatGPT for personalized language practice and culture learning, control group received traditional learning. A questionnaire package, including the cognitive technology use questionnaire (CTUQ), extended mind scale (EMS), distributed cognition questionnaire (DCQ), metacognitive awareness inventory (MAI), and TOEIC pre- and post-tests was administered to collect the data. The difference-in-differences design was adopted and observed a significant treatment effect such that the treatment group had an average increase in mean scores of 37.98 (standard deviation [SD] = 7.80) compared to 19.62 (SD = 7.80) for the control group and, therefore, an average treatment effect of 21.38 (95% confidence interval [18.74, 24. 01]). Findings suggest that ChatGPT promotes cognitive offloading, distributed cognition, and metacognitive awareness (CTUQ mean [M] = 3.701, EMS M = 3.421, DCQ M = 3.721, MAI M = 3.551), and the development of collaborative learning and cultural competence. These results reveal ChatGPT’s potential to reform EFL education, but they also indicate the necessity to mitigate the risks associated with ethical quandaries and over-dependence. Future studies need to create specific scales that can be used for entangled cognition and examine the long-term effects on cognition.
CITATION (APA)
Hsu, L. (2025). Entangled cognition in EFL education: The role of generative AI. Contemporary Educational Technology, 17(4), ep619. https://doi.org/10.30935/cedtech/17621
REFERENCES
- Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). ChatGPT in teaching and learning: A systematic review. Education Sciences, 14(6), Article 643. https://doi.org/10.3390/educsci14060643
- Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8(1), Article 53. https://doi.org/10.1186/s40537-021-00444-8
- Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press. https://doi.org/10.1515/9781400829828
- Aoun, J. E. (2017). Robot-proof: Higher education in the age of artificial intelligence. MIT Press. https://doi.org/10.7551/mitpress/11456.001.0001
- Asio, J. M. R. (2024). AI literacy, self-efficacy, and self-competence among college students: Variances and interrelationships among variables. MOJES: Malaysian Online Journal of Educational Sciences, 12(3), 44–60. https://doi.org/10.22452/aldad.vol12no3.4
- Asio, J. M. R., & Gadia, E. D. (2024). Predictors of student attitudes towards artificial intelligence: Implications and relevance to the higher education institutions. International Journal of Didactical Studies, 5(2), Article 27763. https://doi.org/10.33902/ijods.202427763
- Asio, J. M. R., & Soriano, I. D. (2025). The state of artificial intelligence (AI) use in higher education institutions (HEIs) in the Philippines. In F. D. Mobo (Ed.), Impacts of AI on students and teachers in education 5.0 (pp. 523–552). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8191-5.ch019
- Atchley, P., Pannell, H., Wofford, K., Hopkins, M., & Atchley, R. A. (2024). Human and AI collaboration in the higher education environment: Opportunities and concerns. Cognitive Research: Principles and Implications, 9(1), Article 20. https://doi.org/10.1186/s41235-024-00547-9
- Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How much should we trust differences-in-differences estimates? Quarterly Journal of Economics, 119(1), 249–275. https://doi.org/10.1162/003355304772839588
- Bettayeb, A. M., Abu Talib, M., Sobhe Altayasinah, A. Z., & Dakalbab, F. (2024, July). Exploring the impact of ChatGPT: Conversational AI in education. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1379796
- Bhuyan, B. P., Ramdane-Cherif, A., Tomar, R., & Singh, T. P. (2024). Neuro-symbolic artificial intelligence: A survey. Neural Computing and Applications, 36(21), 12809–12844. https://doi.org/10.1007/s00521-024-09960-z
- Bin-Hady, W. R. A., Ali, J. K. M., & Al-humari, M. A. (2024). The effect of ChatGPT on EFL students’ social and emotional learning. Journal of Research in Innovative Teaching and Learning, 17(2), 243–255. https://doi.org/10.1108/JRIT-02-2024-0036
- Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J. F., Brañas-Garza, P., Butera, L., Douglas, K. M., Everett, J. A. C., Gigerenzer, G., Greenhow, C., Hashimoto, D. A., Holt-Lunstad, J., Jetten, J., Johnson, S., Kunz, W. H., Longoni, C., Lunn, P., … Viale, R. (2024). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS Nexus, 3(6), Article pgae191. https://doi.org/10.1093/pnasnexus/pgae191
- Card, D. (1999). The causal effect of education on earnings. Handbook of Labor Economics, 3, 1801–1863. https://doi.org/10.1016/S1573-4463(99)03011-4
- Chen, C. H., & Chang, C. L. (2024). Effectiveness of AI-assisted game-based learning on science learning outcomes, intrinsic motivation, cognitive load, and learning behavior. Education and Information Technologies, 29(14), 18621–18642. https://doi.org/10.1007/s10639-024-12553-x
- Chen, Z., & Yadollahpour, A. (2024). A new era in cognitive neuroscience: The tidal wave of artificial intelligence (AI). BMC Neuroscience, 25(1), Article 23. https://doi.org/10.1186/s12868-024-00869-w
- Clark, A. (2008). Supersizing the mind: Embodiment, action, and cognitive extension. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195333213.001.0001
- Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
- Ding, L., & Zou, D. (2024). Automated writing evaluation systems: A systematic review of Grammarly, Pigai, and Criterion with a perspective on future directions in the age of generative artificial intelligence. Education and Information Technologies, 29(11), 14151–14203. https://doi.org/10.1007/s10639-023-12402-3
- Duus, R., Cooray, M., & Page, N. C. (2018). Exploring human-tech hybridity at the intersection of extended cognition and distributed agency: A focus on self-tracking devices. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01432
- Escalante, J., Pack, A., & Barrett, A. (2023). AI-generated feedback on writing: Insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20(1), Article 57. https://doi.org/10.1186/s41239-023-00425-2
- Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
- Feng, L. (2024). Investigating the effects of artificial intelligence-assisted language learning strategies on cognitive load and learning outcomes: A comparative study. Journal of Educational Computing Research, 62(8), 1741–1774. https://doi.org/10.1177/07356331241268349
- Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In S. carta (Ed.), Machine learning and the city: Applications in architecture and urban design (pp. 535–545). Wiley. https://doi.org/10.1002/9781119815075.ch45
- Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based writing assistant’s impact on English language learners. Computers and Education, 3, Article 100055. https://doi.org/10.1016/j.caeai.2022.100055
- Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), Article 692. https://doi.org/10.3390/educsci13070692
- Guo, K., Wang, J., & Chu, S. K. W. (2022). Using chatbots to scaffold EFL students’ argumentative writing. Assessing Writing, 54, Article 100666. https://doi.org/10.1016/j.asw.2022.100666
- Hsu, H.-C. (2023). The effect of collaborative prewriting on L2 collaborative writing production and individual L2 writing development. International Review of Applied Linguistics in Language Teaching, 63(1), 499–533. https://doi.org/10.1515/iral-2023-0043
- Hu, W. C., & Škultéty, R. (2024). Unlocking the learning potential: ChatGPT as a virtual platform for cross-interaction in English language learning. Engineering Proceedings, 74(1), Article 59. https://doi.org/10.3390/engproc2024074059
- Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial intelligence-enabled personalized recommendations on learners’ engagement, motivation, and outcomes. Computers and Education, 194, Article 104684. https://doi.org/10.1016/j.compedu.2022.104684
- Huang, J., & Mizumoto, A. (2024). The effects of generative AI usage in EFL classrooms on the L2 motivational self-system. Education and Information Technologies, 30, 6435–6454. https://doi.org/10.1007/s10639-024-13071-6
- Hutchins, E. (1995). Cognition in the wild. MIT Press. https://doi.org/10.7551/mitpress/1881.001.0001
- Hwang, G.-J., & Chen, N.-S. (2023). Editorial position paper: Exploring the potential of generative artificial intelligence in education: Applications, challenges, and future research directions. Educational Technology and Society, 26(2).
- Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86. https://doi.org/10.1257/jel.47.1.5
- Jain, V. (2023). How AI could lead to a better understanding of the brain. Nature, 623(7986), 247–250. https://doi.org/10.1038/d41586-023-03426-3
- Karataş, F., Abedi, F. Y., Ozek Gunyel, F., Karadeniz, D., & Kuzgun, Y. (2024). Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners. Education and Information Technologies, 29, 19343–19366. https://doi.org/10.1007/s10639-024-12574-6
- Kejriwal, M., Santos, H., Mulvehill, A. M., Shen, K., McGuinness, D. L., & Lieberman, H. (2024). Can AI have common sense? Finding out will be key to achieving machine intelligence. Nature, 634(8033), 291–294. https://doi.org/10.1038/d41586-024-03262-z
- Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digital Health, 2(2), Article e0000198. https://doi.org/10.1371/journal.pdig.0000198
- Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), Article 56. https://doi.org/10.1186/s41239-023-00426-1
- Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, Article e253. https://doi.org/10.1017/S0140525X16001837
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
- Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43. https://doi.org/10.1145/3233231
- Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. UCL Press.
- Manzotti, R. (2019). Embodied AI beyond embodied cognition and enactivism. Philosophies, 4(3), Article 39. https://doi.org/10.3390/philosophies4030039
- Mella, P. (2020). Bateson’s model of the mind and the fundamental conjecture on cognition. In Constructing reality. Springer briefs in psychology (pp. 1–20). Springer. https://doi.org/10.1007/978-3-030-44132-6_1
- Mogi, K. (2024). Artificial intelligence, human cognition, and conscious supremacy. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1364714
- Mohebi, L. (2024). Empowering learners with ChatGPT: Insights from a systematic literature exploration. Discover Education, 3(1), Article 36. https://doi.org/10.1007/s44217-024-00120-y
- Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527
- Morais, R. (2023). Rethinking intelligence beyond the anthropic: Pervasive intelligence, entangled cognition and the logic of the included middle. In L. S. G. M. da Costa, & M. T. Loisel (Eds.), Artificial intelligence and human mediation (pp. 105–116). Atlas Press. https://doi.org/10.22545/2024b/B2
- Nguyen, A., Hong, Y., Dang, B., & Huang, X. (2024). Human-AI collaboration patterns in AI-assisted academic writing. Studies in Higher Education, 49(5), 847–864. https://doi.org/10.1080/03075079.2024.2323593
- O’Hara, K., Perry, M., Sellen, A., & Brown, B. (2002a). Exploring the relationship between mobile phones and document activity during business travel. In B. Brown, N. Green, & R. Harper (Eds.), Wireless world. Computer supported cooperative work (pp.180–194). Springer. https://doi.org/10.1007/978-1-4471-0665-4_12
- Piantadosi, S. T. (2021). The computational origin of representation. Minds and Machines, 31(1), 1–58. https://doi.org/10.1007/s11023-020-09540-9
- Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0
- Savage, N. (2019). How AI and neuroscience drive each other forwards. Nature, 571(7766), S15–S17. https://doi.org/10.1038/d41586-019-02212-4
- Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/10.1006/ceps.1994.1033
- Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(1), Article 46. https://doi.org/10.1186/s41239-024-00478-x
- Shanmugasundaram, M., & Tamilarasu, A. (2023). The impact of digital technology, social media, and artificial intelligence on cognitive functions: A review. Frontiers in Cognition, 2. https://doi.org/10.3389/fcogn.2023.1203077
- Shen, Y., & Wang, Y. Y. (2024). Navigating the future of higher education: The transformative role of GenAI. In C. K. Y. Chan, & T. Colloton (Eds.), Generative AI in higher education: The ChatGPT effect. Routledge. https://doi.org/10.1007/s10734-024-01275-1
- Skavronskaya, L., Hadinejad, A., & Cotterell, D. (2023). Reversing the threat of artificial intelligence to opportunity: A discussion of ChatGPT in tourism education. Journal of Teaching in Travel and Tourism, 23(2), 253–258. https://doi.org/10.1080/15313220.2023.2196658
- Slamet, J. (2024). Potential of ChatGPT as a digital language learning assistant: EFL teachers’ and students’ perceptions. Discover Artificial Intelligence, 4(1), Article 46. https://doi.org/10.1007/s44163-024-00143-2
- Stevenson, N., Innes, R. J., Boag, R. J., Miletić, S., Isherwood, S. J. S., Trutti, A. C., Heathcote, A., & Forstmann, B. U. (2024). Joint modelling of latent cognitive mechanisms shared across decision-making domains. Computational Brain and Behavior, 7(1), 1–22. https://doi.org/10.1007/s42113-023-00192-3
- Szabó, F., & Szoke, J. (2024). How does generative AI promote autonomy and inclusivity in language teaching? ELT Journal, 78(4), 478–488. https://doi.org/10.1093/elt/ccae052
- Tariq, S., Iftikhar, A., Chaudhary, P., & Khurshid, K. (2022). Examining some serious challenges and possibility of AI emulating human emotions, consciousness, understanding and ‘self’. Journal of Neurology Philosophy, 1(1), 55–75. https://doi.org/10.5281/zenodo.6637757
- Vallée-Tourangeau, G., & Vallée-Tourangeau, F. (2017). Cognition beyond the classical information processing model: Cognitive interactivity and the systemic thinking model (SysTM). In S. Cowley, & F. Vallée-Tourangeau (Eds.), Cognition beyond the brain (pp. 133–154). Springer. https://doi.org/10.1007/978-3-319-49115-8_7
- Vasiliou, C., Ioannou, A., & Zaphiris, P. (2015). An artifact ecology in a nutshell: A distributed cognition perspective for collaboration and coordination. In J. Abascal, S. Barbosa, M. Fetter, T. Gross, P. Palanque, & M. Winckler (Eds.), Human-computer interaction–INTERACT 2015. INTERACT 2015. Lecture notes in computer science(), vol 9297 (pp. 55–72). Springer. https://doi.org/10.1007/978-3-319-22668-2_5
- Vongkulluksn, V. W., Lu, L., Nelson, M. J., & Xie, K. (2022). Cognitive engagement with technology scale: A validation study. Educational Technology Research and Development, 70(2), 419–445. https://doi.org/10.1007/s11423-022-10098-9
- Wang, C. (2024). Exploring students’ generative AI-assisted writing processes: Perceptions and experiences from native and nonnative English speakers. Technology, Knowledge and Learning, 30, 1825–1846. https://doi.org/10.1007/s10758-024-09744-3
- Wilson, E., Seifert, C., Durning, S. J., Torre, D., & Daniel, M. (2020). Distributed cognition: Interactions between individuals and artifacts. Diagnosis, 7(3), 343–344. https://doi.org/10.1515/dx-2020-0012
- Wooldridge, J. M. (2010). Econometric analysis of cross-section and panel data. MIT Press.
- Xiao, Y., & Zhi, Y. (2023). An exploratory study of EFL learners’ use of ChatGPT for language learning tasks: Experience and perceptions. Languages, 8(3), Article 212. https://doi.org/10.3390/languages8030212
- Yan, Y., Sun, W., & Zhao, X. (2024). Metaphorical conceptualizations of generative artificial intelligence use by Chinese university EFL learners. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1430494
- Yang, L., & Li, R. (2024). ChatGPT for L2 learning: Current status and implications. System, 124, Article 103351. https://doi.org/10.1016/j.system.2024.103351
- Yang, Y., & Xia, N. (2023). Enhancing students’ metacognition via AI-driven educational support systems. International Journal of Emerging Technologies in Learning, 18(24), 133–148. https://doi.org/10.3991/ijet.v18i24.45647
- Zadorozhnyy, A., & Lai, W. Y. W. (2024). ChatGPT and L2 written communication: A game-changer or just another tool? Languages, 9(1), Article 5. https://doi.org/10.3390/languages9010005
- Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(1), Article 28. https://doi.org/10.1186/s40561-024-00316-7
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