Research Article

Entangled cognition in EFL education: The role of generative AI

Liwei Hsu 1 *
More Detail
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
OPEN ACCESS   958 Views   1063 Downloads
Download Full Text (PDF)

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

  1. 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
  2. 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
  3. Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press. https://doi.org/10.1515/9781400829828
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. Clark, A. (2008). Supersizing the mind: Embodiment, action, and cognitive extension. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195333213.001.0001
  18. Clark, A., & Chalmers, D. (1998). The extended mind. Analysis, 58(1), 7–19. https://doi.org/10.1093/analys/58.1.7
  19. 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
  20. 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
  21. 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
  22. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. Hutchins, E. (1995). Cognition in the wild. MIT Press. https://doi.org/10.7551/mitpress/1881.001.0001
  33. 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).
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  42. Lipton, Z. C. (2018). The mythos of model interpretability. Communications of the ACM, 61(10), 36–43. https://doi.org/10.1145/3233231
  43. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. UCL Press.
  44. Manzotti, R. (2019). Embodied AI beyond embodied cognition and enactivism. Philosophies, 4(3), Article 39. https://doi.org/10.3390/philosophies4030039
  45. 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
  46. Mogi, K. (2024). Artificial intelligence, human cognition, and conscious supremacy. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1364714
  47. 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
  48. Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. https://doi.org/10.1006/ceps.1994.1033
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. Wooldridge, J. M. (2010). Econometric analysis of cross-section and panel data. MIT Press.
  70. 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
  71. 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
  72. 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
  73. 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
  74. 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
  75. 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