The Factors Influencing AI Adoption Willingness Among English Language Students in Jordanian Private Universities
DOI:
https://doi.org/10.17507/tpls.1511.14Keywords:
artificial intelligence, English language learning, technology adoption, student perceptions, JordanAbstract
This study examined the factors influencing English language students' willingness to adopt artificial intelligence (AI) in Jordanian private universities. Three key factors were investigated, specifically ease of use, time and psychological risks associated with AI, and task–technology fit (TTF). The study hypothesized that these factors would have a significant positive impact on students’ AI adoption. A quantitative technique was utilized to collect data using a survey. The study sample consisted of 130 English language students. The findings revealed significant positive correlations between students’ willingness to use AI and perceived ease of use as well as perceived time and psychological risks. Contrary to expectations, TTF did not have a significant impact on students’ willingness to use AI. These findings provide valuable insights into the potential for AI adoption in the field of education and the factors that nurture or hinder the integration of this technology.
References
Aleedy, M., Atwell, E., & Meshoul, S. (2022). Using AI Chatbots in Education: Recent Advances Challenges and Use Case. In Artificial Intelligence and Sustainable Computing. Paper presented in 3rd International Conference on Sustainable and Innovative Solutions for Current Challenges in Enginerring & Technology ICSISCET 2021. Algorithms for Intelligent Systems (Pandit, M., Gaur, M. K., Rana, P. S., Tiwari, A., Eds.). Springer Singapore, 661-675. https://doi.org/10.1007/978-981-19-1653-3_50
Algerafi, M., Zhou, Y., Afadda, H., & Wijaya, T. (2023). Understanding the factors influencing higher education students' intention to adopt artificial intelligence-based robots. IEEE, 11, 99752-99764. Doi:10.1109/ACCESS.2023.3314499
Al-Khasawneh, F., Huwari, I., Alqaryouti, M., Alruzzi, K., & Rababah, L. (2024). Factors affecting learner autonomy in the context of English language learning. Cakrawala Pendidikan, 43(1), 140–153.
Al-Mawadieh, R. & Al-Zoabi, T. (2020). The trends of implementing blended learning among Jordanian universities faculty members and the obstacles they face. Zarqa Journal of Research and Humanities, 20(1), 38-48. https://doi.org/10.12816/0055670
Alrashed, Y. et al. (2023). Factors affecting students’ willingness to use gamification in university settings. International Journal of Information and Education Technology, 13(8), 1222-1229. doi: 10.18178/ijiet.2023.13.8.1924
Alyoussef, I. (2021). E-Learning acceptance: The role of task-technology fit as sustainability in higher education. Sustainability, 13(11), 6450. https://doi.org/10.3390/su13116450
Amaireh, H. A., & Rababah, L. M. (2024). Bidenian and Harrisian metaphors: A corpus-based analysis of Joe Biden and Kamala Harris’ political discourse. Jordan Journal of Modern Languages and Literatures, 16(3), 651–671.
Aparicio, M., Oliveira, T., Bacao, F., & Painho, M. (2019). Gamification: A key determinant of massive open online course (MOOC) success. Information Management, 56, 39–54. https://doi.org/10.1016/j.im.2018.06.003
Bauer, R. (1960). Consumer Behavior as Risk Taking, in Dynamic Marketing for a Changing World. Proceedings of the 43rd Conference of the American Marketing Association (R. S. Hancock, Ed) (Chicago, IL), 389–398.
Bezverhny, E., Dadteev, K., Barykin, L., Nemeshaev, S., & Klimov, V. (2020). Use of Chat Bots in Learning Management Systems. Procedia Comput. Sci, 169, 652–655. Doi:10.1016/j.procs.2020.02.195
Cheng, Y., Sun, P., & Chen N. (2018). The essential applications of educational robot: Requirement analysis from the perspectives of experts, researchers, and instructors. Computers & Education, 126, 399–416. https://doi.org/10.1016/j.compedu.2018.07.020
Chocarro, R. Cortiñas, M., & Marcos-Matás, G. (2023). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language bot proactiveness and users’ characteristics. Educ. Stud., 49(2), 295-313. Doi:10.1080/03055698.2020.1850426
Crompton, H., Gregory, K., & Burke, D. (2014). Humanoid robots supporting children’s learning in an early childhood setting. British Journal of Educational Technology, 49, 911–927. https://doi.org/ 10.1111/bjet.12654
Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q, 13, 319–340. https://doi.org/10.2307/249008
Garcia-Madurga, M. & Grillo-Mendez, A. (2023). Artificial intelligence in the tourism industry: An overview of reviews. Administrative Sciences, 13(8), 172. https://doi.org/10.3390/admsci13080172
Goodhue, D. L. (1995). Understanding user evaluations of information systems. Manag. Sci. 41, 1827–1844. https://doi.org/10.1287/mnsc.41.12.1827
Goodhue, D. & Thompson, R. (1995). Task–technology and individual performance. MIS Q, 19, 213–236. http://dx.doi.org/10.2307/249689
Haristiani, N. & Rifai, M. (2021). Chatbot-Based Application Development and Implementation as an Autonomous Language Learning Medium. Indonesia. J. Sci. Technol, 6, 561–576. Doi:10.17509/ijost.v6i3.39150
Huneety, A., Mashaqba, B., Qandail, A., Alshdaifat, A., & Rababah, L. (2024). Refusal strategies by Ammani Arabic monolingual and English-Arabic bilingual speakers. International Journal of Society, Culture and Language, 12(1), 337–352.
Huang, A., Lu, O., & Yang, S. (2022). Effects of artificial Intelligence: Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Comput. Educ, 26, 104684. https://doi.org/10.1016/j.compedu.2022.104684
Jdaitawi, M. & Kan'an, A. (2022). A decade of research on the effectiveness of augmented reality on students with special disability in higher education. Contemporary Educational Technology, 14(1), 1-16. Htps://doi.org/10.30935/cedtech/11369
Jdaitawi, M., Muhaidat, F., Alsharoa, A., Torki, M., & Abdelmoneim, M. (2023). The effectiveness of augmented reality in improving students motivation: An experimental study. Athens Journal of Education, 10(2), 365-379. https://doi.org/10.30958/aje.10-2-10
Kapa, S. (2023). The Role of Artificial Intelligence in the Medical Field. Journal of Computer and Communications, 11, 1-16. https://doi.org/10.4236/jcc.2023.1111001
Khan, S. & Khan, R. (2019). Online assessments: exploring perspectives of university students. Educ. Inf. Technol, 24, 661–677. doi: 10.1007/s10639-018-9797-0
Kim, J. & Gu, K. (2012). The effect of perceived risk and trust on users’ acceptance of cloud computing: mobile cloud computing. J. Soc. Korean Ind. Syst. Eng. 35, 70–76. Retrieved April 21, 2025, from https://www.koreascience.or.kr/article/JAKO201232642192228.page
Kim, J., & Park, N. (2020). Blockchain-based data-preserving AI learning environment model for AI cybersecurity systems in IoT service environments. Appl. Sci., 10, 471. Doi:10.3390/app10144718
Kim, Y., Smith, D., Kim, N., & Chen, T. (2014). Playing with a robot to learn English vocabulary. KAERA Research Forum, 1, 3–8.
Kim, J., & Park, N. (2020). Blockchain-based data-preserving AI learning environment model for AI cybersecurity systems in IoT service environments. Appl. Sci., 10, 471. doi: 10.3390/app10144718
Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
Kumar, P. & Bajaj, R. (2016). Dimension of perceived risk among students at high educational institutes towards online shopping in Punjab. Journal of Internet Banking and Commerce, 21(S5), 1-22. Retrieved March 17, 2025, from http://www.icommercecentral.com
Li, K. (2023). Determinants of college students actual use of AI-Based systems: An extension of the technology acceptance model. Sustainability, 15(6), 5221. https://doi.org/10.3390/su15065221
Li, S. & Gu, X. (2023). A risk framework for human-centered artificial intelligence in education: Based on literature review and Delphi-AHP method. Educational Technology & Society, 26, 187-202. https://doi.org/10.30191/ETS.202301_26(1).0014
Lin, G., Jhang, C., & Wang, Y. (2023). Factors affecting parental intention to use AI-based social robots for children's ESL learning. Education and Information Technologies, 29, 6059-6086. https://doi.org/10.1007/s10639-023-12023-w
Malkawi, N. A. M., Rababah, L. M., Erkir, S., Al-Omari, S. K., & Rababah, M. A. (2024). Effectiveness of English e-learning classes: University students’ perspectives. Journal of Language Teaching and Research, 15(6), 1978–1987.
Muhaidat, F., Alashkar, W., Jdaitawi, M., Abo-Joudeh, M., Hussein, E., Rabab'h, B., Kan'an, A., & Rabab's, B. A meta analysis on augmented reality application for individuals with intellectual disability. International Journal of Information and Education Technology, 12(9), 970-976. https://doi.org/10.18178/ijiet.2022.12.9.1708
Mukherjee, A. (2022). Application of artificial intelligence: Benefits and limitations for human potential and labor-intensive economy-an empirical investigation into pandemic ridden Indian industry. Management Matters, 19(2), 149-166. https://doi.org/10.1108/MANM-02-2022-0034
Naqvi, A. (2020). Artificial intelligence for audit, forensic accounting, and valuation: A strategic perspective. John Wiley & Sons. https://doi.org/10.1002/978111960 1906
Omar, M., Farzeeha, D., & Saari, A. (2022). Gamification in vocational teaching and learning: Perception and readiness among lecturers. International Journal of Education, 14, 140-152. Doi:10.5296/ije.v14i1.19507
Rababah, L. M. (2025a). An experimental study of the effectiveness of role-play in improving fluency in Jordanian EFL students' speaking skills. World Journal of English Language, 15(4), 30–38.
Rababah, L. M. (2025b). Linguistic analysis of gender representations in magazine advertisements: Breaking the semiotic codes. Forum for Linguistic Studies, 7(4), 296–306.
Rababah, L. M., Rababah, M. A., & Al-Khawaldeh, N. N. (2024). Graduate students’ ChatGPT experience and perspectives during thesis writing. International Journal of Engineering Pedagogy, 14(3), 22–35.
Rodway, P. & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, 100150. https://doi.org/10.1016/j.caeai.2023.100150
Roy, R., Babakerkhell, M., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666-125678. Doi:10.1109/ACCESS.2022.3225555
Salleh, S. (2016). Examining the influence of teachers' beliefs towards technology integration in the classroom. The International Journal of Information and Learning Technology, 331, 17-35. https://dx.doi.org/10.1108/IJILT-10-2015-0032
Sánchez-Prieto, J., Cruz-Benito, J., Therón Sánchez, R., & García Peñalvo, F. (2020). Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students. Int. J. Interact. Multimedia. Artif. Intell, 6, 80-86. DOI:10.9781/IJIMAI.2020.11.009
Sandu, N. & Gide, E. (2019). Adoption of AI-Chatbots to Enhance Student Learning Experience in Higher Education in India. In Proceedings of the 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET), Magdeburg, Germany, 26–27, 1–5. Retrieved April 13, 2025, from ieeexplore.ieee.org/document/8937382
Sauerland, W. Broer, J., & A. Breiter, A. (2015). Motivational impact of gamification for mobile learning of kanji. Paper presented at the e-Media World Conference on Educational Media and Technology. Retrieved May 9, 2025, from https://www.learntechlib.org/primary/p/151428/
Shin, S., Ha, M., & Lee, J. K. (2017). High school students' perception of artificial intelligence: focusing on conceptual understanding, emotion and risk perception. J. Learn. Center. Curric. Instr, 17, 289–312. doi: 10.22251/jlcci.2017.17.21.289
Sol, K., Heng, K., & Sok, S. (2024). Using AI in English language education: An exploration of Cambodian EFL university students’ experiences, perceptions and attitude. SSRN Journal 2024 [in press]. http://dx.doi.org/10.2139/ssrn.4687461
Sudaryanto, M., Hendrawan, M., & Andrian, T. (2023). The effect of technology readiness, digital competence, perceived usefulness, and ease of use on accounting students artificial intelligence technology adoption. E3S web of Conference. Doi:10.1051/e3sconf/202338804055
Suh, W., & Ahn, S. (2022). Development and validation of a scale measuring student attitudes toward artificial intelligence. Sage Open, 12(2), 21582440221100463. https://doi.org/10.1177/ 21582440221100463
Sun, J., Ruzic, N., & Philpott, A. (2023). Artificial intelligence technologies and applications for language learning and teaching. Journal China Computer Assistant Language Learning, 14, 1-19. https://doi.org/10.1515/jccall-2023-0015.
Tolksdorf, N., Viertel, F., & Rohlfng, K. (2021). Do shy preschoolers interact differently when learning a language with a social robot? An analysis of interactional behavior and word learning. Frontiers in Robotics and AI, 8, 676123. https://doi.org/10.3389/frobt.2021.676123
Vincent-Lancrin, S. & van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges. Working papers, no. 218. Organization for Economic Co-operation and Development 2020. https://doi.org/10.1787/a6c90fa9-en
Wu, W., Zhang, B., Li, S., & Liu, H. (2022). Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.870777
Zhang, R., Zhao, W., & Wang, Y. (2021). Big data analytics for intelligent online education. J Intell. Fuzzy Syst., 40, 2815–2825. doi: 10.3233/JIFS-189322
Yang, Y., Asaad, Y. & Dwivedi, Y. (2017). Examining the impact of gamification on intention of engagement and brand attitude in the marketing context. Computers in Human Behavior, 73, 459–469, 2017. https://doi.org/10.1016/j.chb.2017.03.066