Enhancing English Language Acquisition through ChatGPT: Use of Technology Acceptance Model in Linguistics
Authors: Hayat Ullah  , Sajid Anwar  , Shah Nawaz Khan  
Published: January 2025

Amidst the ever-changing landscape of English language education, where virtual platforms shape new learning paradigms, this research determines the revolutionary potential of ChatGPT to foster English language acquisition in Pakistan. English is a second language in Pakistan and the learners face multiple challenges in its acquisition. To understand the influence of ChatGPT on English language students, the study relied on quantitative data, using the Technology Acceptance Model (TAM) along with social impact. To test the hypothesized relationships, the study gathered 400 valid responses from English-language students studying at various universities in the southern districts of Khyber Pakhtunkhwa province via purposive sampling. For data analysis, the study applied structure equation modelling through Smart-PLS and found that social influence, perceived usefulness, and perceived ease of use stimulate students’ intentions to use ChatGPT for English language learning. The research fills the gap between English language learners and technology usage, which helps to better understand the connection between AI-based platforms and English learning. This study is helpful for teachers, students, and tech firms to focus on solving students’ learning problems through AI tools.
References:

Abramson, A. (2023). How to use ChatGPT as a learning tool. Monitor on Psychology, 54(3).
Ajzen, I., & Fishbein, M. (1972). Attitudes and normative beliefs as factors influencing behavioural intentions. Journal of personality and social psychology, 21(1), 1.
Alfadda, H. A., & Mahdi, H. S. (2021). Measuring students’ use of Zoom application in language course based on the technology acceptance model (TAM). Journal of Psycholinguistic Research, 50(4), 883-900.
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon.
Almusharraf, A., & Bailey, D. (2023). Predicting attitude, use, and future intentions with translation websites through the TAM framework: a multicultural study among Saudi and South Korean language learners. Computer Assisted Language Learning, 1-28.
Alzoubi, H. (2024). Factors affecting ChatGPT use in education employing TAM: A Jordanian universities’ perspective. International Journal of Data and Network Science, 8(3), 1599-1606.
AlZu'bi, S., Mughaid, A., Quiam, F., & Hendawi, S. (2024). Exploring the capabilities and limitations of chatgpt and alternative big language models. Paper presented at the Artificial Intelligence and Applications.
Bacha, M. S., Kumar, T., Bibi, B. S., & Yunus, M. M. (2021). Using English as a lingua franca in Pakistan: Influences and implications in English Language Teaching (ELT). Asian ESP Journal, 17(2), 155-175.
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62.
Bansal, H., & Khan, R. (2018). A review paper on human computer interaction. International Journal of Advanced Research in Computer Science and Software Engineering, 8(4), 53.
Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and humans, 30(4), 411-420.
Bylund, E., Khafif, Z., & Berghoff, R. (2024). Linguistic and geographic diversity in research on second language acquisition and multilingualism: An analysis of selected journals. Applied Linguistics, 45(2), 308-329.
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P. S., & Sun, L. (2023). A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint arXiv:2303.04226.
Chai, C. S., Lin, P.-Y., Jong, M. S.-Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89-101.
Cong-Lem, N., Soyoof, A., & Tsering, D. (2024). A systematic review of the limitations and associated opportunities of ChatGPT. International Journal of Human–Computer Interaction, 1-16.
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., . . . Soomro, R. B. (2024). Extended TAM based acceptance of AI-Powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
DW, K. E. S. (1999). The psychological origins of perceived usefulness and ease-of-use. Information & management, 35(4), 237-250.
Ferede, B., Elen, J., Van Petegem, W., Hunde, A. B., & Goeman, K. (2022). A structural equation model for determinants of instructors’ educational ICT use in higher education in developing countries: Evidence from Ethiopia. Computers & Education, 188, 104566.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. In: SAGE Publications Sage CA: Los Angeles, CA.
Gael, K. E., & Elmiana, D. S. (2021). Mobile-Assisted Language Learning (MALL) in English language acquisition: a critical literature review. Journal of English Language Teaching Innovations and Materials (Jeltim), 3(2), 76-86.
George, D., & Mallery, P. (2010). SPSS for Windows step by step. A simple study guide and reference (10. Baskı). GEN, Boston, MA: Pearson Education, Inc, 10.
Ghafar, Z. N., Salh, H. F., Abdulrahim, M. A., Farxha, S. S., Arf, S. F., & Rahim, R. I. (2023). The role of artificial intelligence technology on English language learning: A literature review. Canadian Journal of Language and Literature Studies, 3(2), 17-31.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. Englewood Cliff. New jersey, USA, 5(3), 207-2019.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European business review, 31(1), 2-24.
Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil transactions on benchmarks, standards and evaluations, 2(4), 100089.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43, 115-135.
Hosseini, M., Gao, C. A., Liebovitz, D. M., Carvalho, A. M., Ahmad, F. S., Luo, Y., . . . Kho, A. (2023). An exploratory survey about using ChatGPT in education, healthcare, and research. Plos one, 18(10), e0292216.
Iku-Silan, A., Hwang, G.-J., & Chen, C.-H. (2023). Decision-guided chatbots and cognitive styles in interdisciplinary learning. Computers & Education, 201, 104812.
Khan, I. U., Hameed, Z., & Hamayun, M. (2019). Investigating the acceptance of electronic banking in the rural areas of Pakistan: An application of the unified model. Business and Economic Review, 11(3), 57-87.
Khan, I. U., Hameed, Z., Yu, Y., Islam, T., Sheikh, Z., & Khan, S. U. (2018). Predicting the acceptance of MOOCs in a developing country: Application of task-technology fit model, social motivation, and self-determination theory. Telematics and Informatics, 35(4), 964-978.
Kim, A. J.-Y., & Ko, E.-J. (2010). The impact of design characteristics on brand attitude and purchase intention-focus on luxury fashion brands. Journal of the Korean Society of Clothing and Textiles, 34(2), 252-265.
Le, T. M. D., Do, H. T. N., Tran, K. M., Dang, V. T., & Nguyen, B. K. H. (2024). Integrating Tam and UGT to explore students’ motivation for using ChatGPT for learning in Vietnam. Journal of Research in Innovative Teaching & Learning.
Lenneberg, E. H. (1967). Biological foundations of language. In: Wiley.
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.
Liu, H., Chu, H., Huang, Q., & Chen, X. (2016). Enhancing the flow experience of consumers in China through interpersonal interaction in social commerce. Computers in Human Behavior, 58, 306-314.
Lund, B. D., & Wang, T. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Library hi tech news, 40(3), 26-29.
Mahboob, A. (2021). English in Pakistan: Past, present and future. In English in East and South Asia (pp. 75-89): Routledge.
Malik, T., Dwivedi, Y., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., . . . Raghavan, V. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International journal of information management, 71, 102642.
Nunally, J. C., & Bernstein, I. (1978). Psychometric theory, ed. New York McGraw.
Patel, S. B., & Lam, K. (2023). ChatGPT: the future of discharge summaries? The Lancet Digital Health, 5(3), e107-e108.
Peng, M. Y.-P., Xu, Y., & Xu, C. (2023). Enhancing students’ English language learning via M-learning: Integrating technology acceptance model and SOR model. Heliyon, 9(2).
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.
Prislin, R. (2005). Social Influence in Attitude s and Attitude Char1ge. The handbook of attitudes, 671.
Purba, R., Saputra, N., & Fatmawati, E. (2024). Identifying The Implementation of Teaching English to Early Childhood in The Development of Language Acquisition. Pegem Journal of Education and Instruction, 14(2), 126-130.
Raman, R., Mandal, S., Das, P., Kaur, T., Sanjanasri, J., & Nedungadi, P. (2023). University students as early adopters of ChatGPT: Innovation Diffusion Study.
Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2024). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097.
Sallam, M., Elsayed, W., Al-Shorbagy, M., Barakat, M., El Khatib, S., Ghach, W., . . . Malaeb, D. (2024). ChatGPT usage and attitudes are driven by perceptions of usefulness, ease of use, risks, and psycho-social impact: a study among university students in the UAE. Paper presented at the Frontiers in Education.
Salloum, S. A., Aljanada, R. A., Alfaisal, A. M., Al Saidat, M. R., & Alfaisal, R. (2024). Exploring the Acceptance of ChatGPT for Translation: An Extended TAM Model Approach. In Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom (pp. 527-542): Springer.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632): Springer.
Saunders., M., Lewis., P., & Thornhill, A. (2016). Research Methods for Business Students (7th ed.). Italy: Pearson, Harlow. .
Shaengchart, Y. (2023). A conceptual review of TAM and ChatGPT usage intentions among higher education students. Advance Knowledge for Executives, 2(3), 1-7.
Thao, L. T., Thuy, P. T., Thi, N. A., Yen, P. H., Thu, H. T. A., & Tra, N. H. (2023). Impacts of Emotional Intelligence on Second Language Acquisition: English-Major Students’ Perspectives. SAGE Open, 13(4), 21582440231212065.
Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024). What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT. Interactive Technology and Smart Education, 21(3), 333-355.
Urip, S., Reli, H., Faruq, U. M., & Mujiyono, W. (2022). Determinants of technology acceptance model (TAM) towards ICT use for English language learning. Journal of Language and Education, 8(2 (30)), 17-30.
Van Dis, E. A., Bollen, J., Zuidema, W., Van Rooij, R., & Bockting, C. L. (2023). ChatGPT: five priorities for research. Nature, 614(7947), 224-226.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
Whitford, E. (2022). A Computer Can Now Write Your College Essay, Maybe Better Than You Can. Forbes com.
Wong, E. Y.-c., Hui, R. T.-y., & Kong, H. (2023). Perceived usefulness of, engagement with, and effectiveness of virtual reality environments in learning industrial operations: the moderating role of openness to experience. Virtual Reality, 27(3), 2149-2165.
Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student attitudes towards Chat GPT: A technology acceptance model survey. International Educational Review, 1(1), 57-83.