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Unpacking the Dynamics of AI-Based Language Learning: Flow, Grit, and Resilience in Chinese EFL Contexts

Zhai, Xiuwen, Zhao, Ruijie, Jiang, Yueying, Wu, Hanwei·2024·Behavioral Sciences
DOI 10.3390/bs14090838PMC PMC11428701
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Abstract

Artificial intelligence and positive psychology play crucial roles in education, yet there is limited research on how these psychological factors influence learners’ use of AI, particularly in language education. Grounded in self-determination theory, this study investigates the factors influencing Chinese English learners’ intention to use AI for language learning. Utilizing structural equation modeling, this research examines the mediating roles of grit, flow, and resilience in the relationship between basic psychological needs and the intention to use AI. Data were analyzed using AMOS 26 and SPSS 26. The findings reveal that flow, grit, and resilience mediate the relationship between basic psychological needs and the intention to adopt AI tools for language learning. This study provides valuable insights into how educational environments can be designed to fulfill psychological needs, thereby fostering greater engagement and acceptance of AI in language education.

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Research works printed but not linked: 68

  1. Hong, 2017, Intrinsic Motivation of Chinese Learning in Predicting Online Learning Self-Efficacy and Flow Experience Relevant to Students’ Learning Progress, Comput. Assist. Lang. Learn., 30, 552, 10.1080/09588221.2017.1329215 · DOI
  2. Xiuwen, 2021, An Overview of the Utilization of Tiktok to Improve Oral English Communication Competence among Efl Undergraduate Students, Univ. J. Educ. Res., 9, 1439
  3. Ahmad, S.F., Rahmat, M.K., Mubarik, M.S., Alam, M.M., and Hyder, S.I. (2021). Artificial Intelligence and Its Role in Education. Sustainability, 13. · DOI
  4. Ebadi, 2024, Examining the Roles of Social Presence and Human-Likeness on Iranian Efl Learners’ Motivation Using Artificial Intelligence Technology: A Case of Csiec Chatbot, Interact. Learn. Environ., 32, 655, 10.1080/10494820.2022.2096638 · DOI
  5. Gao, 2024, Exploring Male English Major’s Motivation Trajectory through Complex Dynamic Systems Theory, Curr. Psychol., 43, 9089, 10.1007/s12144-023-05062-6 · DOI
  6. Wang, Q., Sun, F., Wang, X., and Gao, Y. (2023). Exploring Undergraduate Students’ Digital Multitasking in Class: An Empirical Study in China. Sustainability, 15. · DOI
  7. Mayer, 2022, Instructional Media and Instructional Methods in Digital Language Learning: Are We Asking the Right Questions?, Biling. Lang. Cogn., 25, 396, 10.1017/S1366728921000559 · DOI
  8. Naidu, S. (2003). Learning & Teaching with Technology: Principles and Practices, Psychology Press.
  9. Songsiengchai, 2023, Leveraging Artificial Intelligence (Ai): Chat Gpt for Effective English Language Learning among Thai Students, Eng. Lang. Teach., 16, 68, 10.5539/elt.v16n11p68 · DOI
  10. Zhai, 2021, Utilization of Online Fan-Fiction to Improve English Literacy Skills among Efl Undergraduate Students, Int. J. Acad. Res. Progress. Educ. Dev., 10, 139
  11. Qiu, 2023, Trends in Vr/Ar Technology-Supporting Language Learning from 2008 to 2019: A Research Perspective, Interact. Learn. Environ., 31, 2090, 10.1080/10494820.2021.1874999 · DOI
  12. Shadiev, R., and Yang, M. (2020). Review of Studies on Technology-Enhanced Language Learning and Teaching. Sustainability, 12. · DOI
  13. Yang, 2007, Technology-Enhanced Language Learning: A Case Study, Comput. Hum. Behav., 23, 860, 10.1016/j.chb.2006.02.015 · DOI
  14. Warschauer, 1995, Comparing Face-to-Face and Electronic Discussion in the Second Language Classroom, Calico J., 13, 7, 10.1558/cj.v13i2-3.7-26 · DOI
  15. Allen, 2024, Chatgpt over My Friends: Japanese English-as-a-Foreign-Language Learners’ Preferences for Editing and Proofreading Strategies, Relc J., 17, 00336882241262533, 10.1177/00336882241262533 · DOI
  16. Nami, 2020, Educational Smartphone Apps for Language Learning in Higher Education: Students’ Choices and Perceptions, Australas. J. Educ. Tec., 36, 82
  17. Wei, L. (2023). Artificial Intelligence in Language Instruction: Impact on English Learning Achievement, L2 Motivation, and Self-Regulated Learning. Front. Psychol., 14. · DOI · Paper page
  18. Deci, 2012, Self-Determination Theory, Handb. Theor. Soc. Psychol., 1, 416
  19. Deci, E.L., and Ryan, R.M. (2013). Intrinsic Motivation and Self-Determination in Human Behavior, Springer Science & Business Media.
  20. Wang, X., and Reynolds, B.L. (2024). Beyond the Books: Exploring Factors Shaping Chinese English Learners’ Engagement with Large Language Models for Vocabulary Learning. Educ. Sci., 14. · DOI
  21. Wang, 2024, Exploring Chinese Efl Learners’ Engagement with Large Language Models: A Self-Determination Theory Perspective, Learn. Motiv., 87, 102014, 10.1016/j.lmot.2024.102014 · DOI
  22. Sudina, 2021, Academic Perseverance in Foreign Language Learning: An Investigation of Language-Specific Grit and Its Conceptual Correlates, Mod. Lang. J., 105, 829, 10.1111/modl.12738 · DOI
  23. 2018, What Shall We Do About Grit? A Critical Review of What We Know and What We Don’t Know, Educ. Res., 47, 606, 10.3102/0013189X18801322 · DOI
  24. Chen, 2021, Grit and Motivation for Learning English among Japanese University Students, System, 96, 102411, 10.1016/j.system.2020.102411 · DOI
  25. Feng, 2020, Persistence in Language Learning: The Role of Grit and Future Self-Guides, Learn. Individ. Differ., 81, 101904, 10.1016/j.lindif.2020.101904 · DOI
  26. Khajavy, 2021, A Closer Look at Grit and Language Mindset as Predictors of Foreign Language Achievement, Stud. Second Lang. Acq., 43, 379, 10.1017/S0272263120000480 · DOI
  27. Zarrinabadi, 2022, L2 Grit and Language Mindsets as Predictors of Efl Learners’ Attitudes toward Effectiveness and Value of Call, Comput. Assist. Lang. L., 43, 1
  28. Csikszentmihalyi, 1990, Literacy and Intrinsic Motivation, Daedalus, 119, 115
  29. Egbert, 2004, A Study of Flow Theory in the Foreign Language Classroom, Can. Mod. Lang. Rev., 60, 549, 10.3138/cmlr.60.5.549 · DOI
  30. Liu, Y., and Huang, X. (2021). Effects of Basic Psychological Needs on Resilience: A Human Agency Model. Front. Psychol., 12. · DOI
  31. Cho, 2018, Task Complexity and Modality: Exploring Learners’ Experience from the Perspective of Flow, Mod. Lang. J., 102, 162, 10.1111/modl.12460 · DOI
  32. Csikszentmihalyi, 1997, Flow and the Psychology of Discovery and Invention, HarperPerennial, 39, 1
  33. 2013, How Basic Need Satisfaction and Dispositional Motives Interact in Predicting Flow Experience in Sport, J. Appl. Soc. Psychol., 43, 687, 10.1111/j.1559-1816.2013.01045.x · DOI
  34. Mitchell, 2001, Weighing the Ways of the Flow: Twentieth Century Language Instruction, Mod. Lang. J., 85, 26, 10.1111/0026-7902.00095 · DOI
  35. Dewaele, J.-M., Albakistani, A., and Kamal Ahmed, I. (2022). Is Flow Possible in the Emergency Remote Teaching Foreign Language Classroom?. Educ. Sci., 12. · DOI
  36. Ibrahim, Z. (2020). Sustained Flow: Affective Obsession in Second Language Learning. Front. Psychol., 10. · DOI
  37. Masten, 1990, Resilience and Development: Contributions from the Study of Children Who Overcome Adversity, Dev. Psychopathol., 2, 425, 10.1017/S0954579400005812 · DOI
  38. Wagnild, 1993, Development and Psychometric, J. Nurs. Meas., 1, 165
  39. Kim, 2019, Role of Resilience in (De) Motivation and Second Language Proficiency: Cases of Korean Elementary School Students, J. Psycholinguist. Res., 48, 371, 10.1007/s10936-018-9609-0 · DOI
  40. Aldosari, M.S., and Alsager, H.N. (2023). A Step toward Autonomy in Education: Probing into the Effects of Practicing Self-Assessment, Resilience, and Creativity in Task Supported Language Learning. Bmc Psychol., 11. · DOI
  41. Nguyen, 2015, Resilience in Language Learners and the Relationship to Storytelling, Cogent Educ., 2, 991160, 10.1080/2331186X.2014.991160 · DOI
  42. Zarrinabadi, 2022, Resilience in Language Classrooms: Exploring Individual Antecedents and Consequences, System, 109, 102892, 10.1016/j.system.2022.102892 · DOI
  43. Kim, 2017, The Impact of Resilience on L2 Learners’ Motivated Behaviour and Proficiency in L2 Learning, Educ. Stud., 43, 1, 10.1080/03055698.2016.1237866 · DOI
  44. Guay, 2022, Applying Self-Determination Theory to Education: Regulations Types, Psychological Needs, and Autonomy Supporting Behaviors, Can. J. Sch. Psychol., 37, 75, 10.1177/08295735211055355 · DOI · Paper page
  45. McEown, 2019, Self-Determination for All Language Learners: New Applications for Formal Language Education, System, 86, 102124, 10.1016/j.system.2019.102124 · DOI
  46. Noels, 2003, Why Are You Learning a Second Language? Motivational Orientations and Self-Determination Theory, Lang. L., 53, 33, 10.1111/1467-9922.53223 · DOI
  47. Al-Hoorie, A.H., Oga-Baldwin, W.L.Q., Hiver, P., and Vitta, J.P. (2022). Self-Determination Mini-Theories in Second Language Learning: A Systematic Review of Three Decades of Research. Lang. Teach. Res., 13621688221102686. · DOI
  48. Huescar Hernandez, E., Moreno-Murcia, J.A., Cid, L., Monteiro, D., and Rodrigues, F. (2020). Passion or Perseverance? The Effect of Perceived Autonomy Support and Grit on Academic Performance in College Students. Int. J. Env. Res. Pub. He., 17. · DOI
  49. Shirvan, 2022, Modeling the Interplay of Efl Learners’ Basic Psychological Needs, Grit and L2 Achievement, J. Multiling. Multicul., 45, 2831, 10.1080/01434632.2022.2075002 · DOI
  50. 2022, Predicting Students’ Basic Psychological Need Profiles through Motivational Appeals: Relations with Grit and Well-Being, Learn. Individ. Differ., 97, 102162, 10.1016/j.lindif.2022.102162 · DOI
  51. Gao, 2023, Grit and Learning: Understanding the Sequential Learning Behaviours in Snap!, J. Comput. Assist. Lear., 39, 603, 10.1111/jcal.12766 · DOI
  52. Lee, 2023, Digital Storytelling Outcomes, Emotions, Grit, and Perceptions among Efl Middle School Learners: Robot-Assisted Versus Powerpoint-Assisted Presentations, Comput. Assist. Lang. Learn., 36, 1088, 10.1080/09588221.2021.1969410 · DOI
  53. Sampedro, 2013, The Role of Basic Psychological Needs in Predicting Dispositional Flow of Basketball Players in Training. Differences by Sex, Rev. De Psicol. Del Depor., 22, 187
  54. Hu, L., Hei, D., Wang, H., and Dai, X. (2023). Chinese College Students Collaborative Mobile-Assisted Language Learning Experience and Flow as a Key Factor for Further Adoption. Front. Psychol., 14. · DOI
  55. Tai, 2024, Practicing Drum on Vr to Promote Rhythm Performance: Exploring the Learning Progress Related to Incremental Belief of Rhythm, Gameplay Anxiety, Flow Experience, and Perceived Learning Value, Entertain. Comput., 48, 100607, 10.1016/j.entcom.2023.100607 · DOI
  56. Neufeld, A., and Malin, G. (2019). Exploring the Relationship between Medical Student Basic Psychological Need Satisfaction, Resilience, and Well-Being: A Quantitative Study. Bmc Med. Educ., 19. · DOI
  57. Lee, 2023, Developing Digital Resilience: An Educational Intervention Improves Elementary Students’ Response to Digital Challenges, Comput. Educ. Open, 5, 100144, 10.1016/j.caeo.2023.100144 · DOI
  58. Versteeg, M., Kappe, R.F., and Knuiman, C. (2022). Predicting Student Engagement: The Role of Academic Belonging, Social Integration, and Resilience During Covid-19 Emergency Remote Teaching. Front. Public Health, 10. · DOI
  59. Zheng, 2024, Identifying Key Features of Resilient Students in Digital Reading: Insights from a Machine Learning Approach, Educ. Inf. Technol., 29, 2277, 10.1007/s10639-023-11908-0 · DOI
  60. Ryan, R.M., and Deci, E.L. (2017). Self-Determination Theory: Basic Psychological Needs in Motivation, Development, and Wellness, Guilford Press. · DOI · Paper page
  61. Collier, J.E. (2020). Applied Structural Equation Modeling Using Amos: Basic to Advanced Techniques, Routledge. · DOI
  62. Wheaton, 1977, Assessing Reliability and Stability in Panel Models, Sociol. Methodol., 8, 84, 10.2307/270754 · DOI
  63. Tabachnick, B.G., Fidell, L.S., and Ullman, J.B. (2013). Using Multivariate Statistics, Pearson.
  64. Yang, 2022, Towards Understanding Teacher Mentoring, Learner Wcf Beliefs, and Learner Revision Practices through Peer Review Feedback: A Sociocultural Perspective, J. Lang. Educ., 8, 58, 10.17323/jle.2022.15962 · DOI
  65. Wang, 2024, Unveiling the Tapestry of Teacher Belief Research: Tracing the Present and Forging the Future through Bibliometric Analysis, Curr. Psychol., 43, 15659, 10.1007/s12144-023-05546-5 · DOI
  66. Wang, X., Gao, Y., Wang, Q., and Zhang, P. (2024). Relationships between Self-Efficacy and Teachers’ Well-Being in Middle School English Teachers: The Mediating Role of Teaching Satisfaction and Resilience. Behav. Sci., 14. · DOI
  67. Gao, 2024, How Do Praxis-Oriented Themes Inform the Unity of Language Teachers’ Beliefs, Practices, and Identities? A Narrative Inquiry, Chin. J. Appl. Ling., 47, 83, 10.1515/CJAL-2024-0106 · DOI
  68. Wang, X., Sun, F., Wang, Q., and Li, X. (2022). Motivation and Affordance: A Study of Graduate Students Majoring in Translation in China. Front. Educ., 7. · DOI

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Metadata

Authors
Zhai, X., Zhao, R., Jiang, Y., & Wu, H.
Affiliations
  • School of Foreign Language Studies, Guangxi University, Nanning 530004, China
  • School of Foreign Studies, Xi’an Jiaotong University, Xi’an 710049, China
  • Institute for Advanced Studies in Humanities and Social Science, Beihang University, Beijing 100191, China
  • School of Foreign Studies, Hunan Normal University, Changsha 410000, China
ORCID
Venue
Behavioral Sciences
Publisher
MDPI AG
Published
2024-09-19
Volume
14
Issue
9
Pages
838
ISSN
2076-328X
DOI
10.3390/bs14090838
PMC
PMC11428701
Cited by
50
References
14
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • Research and Practice on Digital Teaching of the ‘Introduction to Linguistics’ Course Based on ‘Knowledge Graph + Large Language Models (LLMs)’ (2024JGA112, XWK202429, CWZD202404)
  • Exploration and Practice of Training Reserve Talents as ‘Strategic Social Scientists’ for ASEAN Countries and Regions (2024JGA112, XWK202429, CWZD202404)
  • Research on ASEAN Strategies of Major Extraterritorial Countries: Major Project of the 2024 Open Topics of the China-ASEAN Regional Development Collaborative Innovation Center (2024JGA112, XWK202429, CWZD202404)

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