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Article

Assessing the Adoption and Influence of Artificial Intelligence as a Learning Partner among English Learners in Hail

Omsalma Ibrahim Mohamed Ahmed ORCID ,  Redhwan Qasem Ghaleb Rashed ORCID ,  Sara Alrefaee ORCID ,  Afrah Aboalbasher Mohamed Babiker ,  Hiwaida Mohamed Elrayah

    Received: 13 March 2026; Revised: 7 April 2026; Accepted: 16 April 2026; Published: 30 April 2026

    Abstract

    Generative artificial intelligence tools offer transformative potential for English language learners, yet the factors driving their adoption remain insufficiently understood, particularly in Arab higher education contexts. This study examined AI tool adoption among undergraduate English learners at the University of Hail, Saudi Arabia, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the theoretical framework. A quantitative survey of 318 students tested nine hypothesized relationships between seven UTAUT2 constructs and two outcomes: behavioral intention and use behavior. Partial least squares structural equation modeling (PLS-SEM) revealed exceptional explanatory power, accounting for 80.1% of the variance in behavioral intention and 80.9% in use behavior. Seven of nine hypotheses were supported. Habit emerged as the dominant predictor, exerting the strongest total effect on use behavior (β = 0.518), operating through both conscious intention and automatic behavioral pathways. Price value, reconceptualized to capture time and effort costs rather than monetary ones, was the second-strongest predictor of intention, followed by hedonic motivation and performance expectancy. Contrary to expectations, effort expectancy and social influence were non-significant. These findings extend UTAUT2 to generative AI in language learning, challenge assumptions about universal predictor applicability, and offer practical guidance for educators and policymakers seeking to promote sustained, effective AI integration in English language education.

    Keywords

    References

    1. 1. Alotaibi, H.M.; Sonbul, S.S.; El-Dakhs, D.A. Factors Influencing the Acceptance and Use of ChatGPT among English as a Foreign Language Learners in Saudi Arabia. Humanit. Soc. Sci. Commun. 2025, 12, 628. DOI: https://doi.org/10.1057/s41599-025-04945-2
    2. 2. Zou, B.; Lyu, Q.; Han, Y.; et al. Exploring Students’ Acceptance of an Artificial Intelligence Speech Evaluation Program for EFL Speaking Practice: An Application of the Integrated Model of Technology Acceptance. Comput. Assist. Lang. Learn. 2023, 38, 1366–1391. DOI: https://doi.org/10.1080/09588221.2023.2278608
    3. 3. Strzelecki, A. Students’ Acceptance of ChatGPT in Higher Education: An Extended Unified Theory of Acceptance and Use of Technology. Innov. High. Educ. 2024, 49, 223–245. DOI: https://doi.org/10.1007/s10755-023-09686-1
    4. 4. Zheng, Y.; Wang, Y.; Liu, K.S.-X.; et al. Examining the Moderating Effect of Motivation on Technology Acceptance of Generative AI for English as a Foreign Language Learning. Educ. Inf. Technol. 2024, 29, 23547–23575. DOI: https://doi.org/10.1007/s10639-024-12763-3
    5. 5. Boudouaia, A.; Mouas, S.; Kouider, B. A Study on ChatGPT-4 as an Innovative Approach to Enhancing English as a Foreign Language Writing Learning. J. Educ. Comput. Res. 2024, 62, 1289–1317. DOI: https://doi.org/10.1177/07356331241247465
    6. 6. Hu, X.; Gong, W. Modeling Chinese EFL Learners’ Intention to Use Generative AI for L2 Writing through an Integrated Model of the TAM and TTF. Educ. Inf. Technol. 2025, 30, 18157–18179. DOI: https://doi.org/10.1007/s10639-025-13505-9
    7. 7. Zhou, Q.; Hashim, H.; Sulaiman, N.A. Supporting English Speaking Practice in Higher Education: The Impact of AI Chatbot-Integrated Mobile-Assisted Blended Learning Framework. Educ. Inf. Technol. 2025, 30, 14629–14660. DOI: https://doi.org/10.1007/s10639-025-13401-2
    8. 8. Alsakaker, S.M. Investigating EFL Learners’ Perceptions of Using AI to Enhance English Vocabulary Acquisition Based on the Technology Acceptance Model. Forum Linguist. Stud. 2025, 7, 1067–1077. DOI: https://doi.org/10.30564/fls.v7i2.8593
    9. 9. Mariappan, R.; Tan, K.H.; Philip, B. Timely Adoption of Grammarly to Cultivate Autonomous Learning Culture. J. Educ. Learn. 2025, 19, 751–756. DOI: https://doi.org/10.11591/edulearn.v19i2.21124
    10. 10. Wang, Q.; Amini, M.; Fu, Z. AI Acceptance and Chinese EFL Learners’ Behavioral Engagement with Mediating Effects of Motivation. Sci. Rep. 2025, 15, 33310. DOI: https://doi.org/10.1038/s41598-025-11305-2
    11. 11. Xu, X.; Thien, L.M. Unleashing the Power of Perceived Enjoyment: Exploring Chinese Undergraduate EFL Learners’ Intention to Use ChatGPT for English Learning. J. Appl. Res. High. Educ. 2024, 17, 578–593. DOI: https://doi.org/10.1108/JARHE-12-2023-0555
    12. 12. Alsaedi, N.S. Exploring ChatGPT’s Role in EFL Learning through the Technology Acceptance Model: Perspectives from Saudi Students. Contemp. Educ. Technol. 2025, 17, ep594. DOI: https://doi.org/10.30935/cedtech/17302
    13. 13. Zaim, M.; Arsyad, S.; Waluyo, B.; et al. AI-Powered EFL Pedagogy: Integrating Generative AI into University Teaching Preparation through UTAUT and Activity Theory. Comput. Educ. Artif. Intell. 2024, 7, 100335. DOI: https://doi.org/10.1016/j.caeai.2024.100335
    14. 14. Aksakalli, C.; Daşer, Z. Unlocking EFL Learners’ Insights into ChatGPT Use for L2 Writing: The Impacts of Usage Frequency and Gender Variations. Curr. Psychol. 2025, 44, 7957–7977. DOI: https://doi.org/10.1007/s12144-025-07437-3
    15. 15. Alrishan, A.M.H. Predicting EFL Students’ Use of Artificial Intelligence Tool in Advancing Their Writing Skills. Emerg. Sci. J. 2025, 9, S319–S333. DOI: https://doi.org/10.28991/esj-2025-sied1-018
    16. 16. Moradi, H. Integrating AI in Higher Education: Factors Influencing ChatGPT Acceptance among Chinese University EFL Students. Int. J. Educ. Technol. High. Educ. 2025, 22, e30. DOI: https://doi.org/10.1186/s41239-025-00530-4
    17. 17. Venkatesh, V.; Thong, J.Y.L.; Xu, X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157–178. DOI: https://doi.org/10.2307/41410412
    18. 18. Almusharraf, A.; Bailey, D.; Almusharraf, N.; et al. Students’ Perceptions of Generative AI in EFL Writing: Strategies, Self-Efficacy, Satisfaction and Behavioural Intention. Australas. J. Educ. Technol. 2025, 41, 18–36. DOI: https://doi.org/10.14742/ajet.10045
    19. 19. Alharbi, J.M. Adoption of Artificial Intelligence Tools for English Language Learning among Saudi EFL University Students: The Moderating Role of Faculty. J. Ecohumanism 2025, 4, 1–18. DOI: https://doi.org/10.62754/joe.v4i2.6349
    20. 20. Jamshed, M.; Almashy, A.; Albedah, F.; et al. Assessing the Efficacy of Artificial Intelligence-Enabled EFL Learning and Teaching in Saudi Arabia: Perceptions, Perspectives, and Prospects. J. Lang. Teach. Res. 2024, 15, 1931–1940. DOI: https://doi.org/10.17507/jltr.1506.18
    21. 21. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319–340. DOI: https://doi.org/10.2307/249008
    22. 22. Venkatesh, V.; Morris, M.G.; Davis, G.B.; et al. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478. DOI: https://doi.org/10.2307/30036540
    23. 23. Alkolaly, M.M.; Hatamleh, H.A.; Al-Shamali, N.; et al. Exploring the Variation between Lecturers’ and Students’ Attitude towards Leveraging Generative Artificial Intelligence Systems in Foreign Language Teaching and Learning. Int. J. Educ. Reform 2025. DOI: https://doi.org/10.1177/10567879251403530
    24. 24. Parviz, M.; Arthur, F. Exploring EFL Teachers’ Behavioral Intentions to Integrate GenAI Applications: Insights from PLS-SEM and fsQCA. Hum. Behav. Emerg. Technol. 2025, 2025, 5582099. DOI: https://doi.org/10.1155/hbe2/5582099
    25. 25. Becker, G.S. A Theory of the Allocation of Time. Econ. J. 1965, 75, 493–517. DOI: https://doi.org/10.2307/2228949
    26. 26. Verplanken, B.; Aarts, H. Habit, Attitude, and Planned Behaviour: Is Habit an Empty Construct or an Interesting Case of Goal-Directed Automaticity? Eur. Rev. Soc. Psychol. 1999, 10, 101–134. DOI: https://doi.org/10.1080/14792779943000035
    27. 27. Limayem, M.; Hirt, S.G.; Cheung, C.M.K. How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Q. 2007, 31, 705–737. DOI: https://doi.org/10.2307/25148817
    28. 28. King, W.R.; He, J. A Meta-Analysis of the Technology Acceptance Model. Inf. Manag. 2006, 43, 740–755. DOI: https://doi.org/10.1016/j.im.2006.05.003
    29. 29. Srite, M.; Karahanna, E. The Role of Espoused National Cultural Values in Technology Acceptance. MIS Q. 2006, 30, 679–704. DOI: https://doi.org/10.2307/25148745
    30. 30. Ryan, R.M.; Deci, E.L. Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being. Am. Psychol. 2000, 55, 68–78. DOI: https://doi.org/10.1037/0003-066X.55.1.68
    31. 31. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Addison-Wesley: Reading, MA, USA, 1975.
    32. 32. Sheeran, P. Intention–Behavior Relations: A Conceptual and Empirical Review. Eur. Rev. Soc. Psychol. 2002, 12, 1–36. DOI: https://doi.org/10.1080/14792772143000003
    33. 33. Al-Bukhrani, M.A.; Alrefaee, Y.M.H.; Tawfik, M. Adoption of AI Writing Tools among Academic Researchers: A Theory of Reasoned Action Approach. PLoS One 2025, 20, e0313837. DOI: https://doi.org/10.1371/journal.pone.0313837
    34. 34. Alshaie, F.S.; Alshdokhi, K.A.; Alrefaee, Y.M.H.A. Faculty Perceptions and Implementation Strategies for AI Personalized Learning Systems at Hail University. Discov. Sustain. 2026. DOI: https://doi.org/10.1007/s43621-026-03172-2
    35. 35. Aljabr, F.; Zakarneh, B.; Annamalai, N.; et al. Integrating AI: Challenges and Opportunities in Teaching English Writing Skills. World J. Engl. Lang. 2025, 15, 371. DOI: https://doi.org/10.5430/wjel.v15n5p371
    36. 36. Almutairi, Y.M.N.; Al-Saad, A.F.; Elmelegy, R.I.; et al. Fourth Industrial Revolution and Higher Education in the Kingdom of Saudi Arabia. Front. Educ. 2025, 9, 1487634. DOI: https://doi.org/10.3389/feduc.2024.1487634
    37. 37. Kock, N.; Hadaya, P. Minimum Sample Size Estimation in PLS-SEM: The Inverse Square Root and Gamma-Exponential Methods. Inf. Syst. J. 2018, 28, 227–261. DOI: https://doi.org/10.1111/isj.12131
    38. 38. Hair Jr., J.F.; Hult, G.T.M.; Ringle, C.M.; et al. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; SAGE Publications: Thousand Oaks, CA, USA, 2017.
    39. 39. Nunnally, J.C.; Bernstein, I.H. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994.
    40. 40. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. DOI: https://doi.org/10.1177/002224378101800104
    41. 41. Henseler, J.; Ringle, C.M.; Sarstedt, M. A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. DOI: https://doi.org/10.1007/s11747-014-0403-8
    42. 42. Tamilmani, K.; Rana, N.P.; Prakasam, N.; et al. The Battle of Brain vs. Heart: A Literature Review and Meta-Analysis of ‘Hedonic Motivation’ Use in UTAUT2. Int. J. Inf. Manag. 2019, 46, 222–235. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.01.008
    43. 43. Hu, L.T.; Bentler, P.M. Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Struct. Equ. Model. 1999, 6, 1–55. DOI: https://doi.org/10.1080/10705519909540118
    44. 44. Hair, J.F.; Risher, J.J.; Sarstedt, M.; et al. When to Use and How to Report the Results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. DOI: https://doi.org/10.1108/EBR-11-2018-0203
    45. 45. Chin, W.W. The Partial Least Squares Approach for Structural Equation Modeling. In Modern Methods for Business Research; Marcoulides, G.A., Ed.; Lawrence Erlbaum Associates: Mahwah, NJ, USA, 1998; pp. 295–336.
    46. 46. Geisser, S. A Predictive Approach to the Random Effect Model. Biometrika 1974, 61, 101–107. DOI: https://doi.org/10.1093/biomet/61.1.101
    47. 47. Stone, M. Cross-Validatory Choice and Assessment of Statistical Predictions. J. R. Stat. Soc. Ser. B 1974, 36, 111–133. DOI: https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
    48. 48. Shmueli, G.; Sarstedt, M.; Hair, J.F.; et al. Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict. Eur. J. Mark. 2019, 53, 2322–2347. DOI: https://doi.org/10.1108/EJM-02-2019-0189
    49. 49. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Routledge: New York, NY, USA, 1988.
    50. 50. Al-Azawei, A.; Alowayr, A. Predicting the Intention to Use and Hedonic Motivation for Mobile Learning: A Comparative Study in Two Middle Eastern Countries. Technol. Soc. 2020, 62, 101325. DOI: https://doi.org/10.1016/j.techsoc.2020.101325
    51. 51. Strack, F.; Deutsch, R. Reflective and Impulsive Determinants of Social Behavior. Pers. Soc. Psychol. Rev. 2004, 8, 220–247. DOI: https://doi.org/10.1207/s15327957pspr0803_1
    52. 52. Hofstede, G. Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations, 2nd ed.; Sage Publications: Thousand Oaks, CA, USA, 2001.
    53. 53. Alshammari, S. Determining the Factors That Affect the Use of Virtual Classrooms: A Modification of the UTAUT Model. J. Inf. Technol. Educ. Res. 2021, 20, 117–135. DOI: https://doi.org/10.28945/4709
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