Artificial Intelligence in Higher Education: A Cross-Cultural Examination of Students’ Behavioral Intentions and Attitudes
DOI:
https://doi.org/10.19173/irrodl.v25i3.7703Keywords:
artificial intelligence, higher education, technology acceptance model, attitudes, behavioral intentionsAbstract
Artificial intelligence (AI) has undergone considerable advancement in the contemporary period and represents an emerging technology in higher education. Cultural contexts significantly shape individuals’ perceptions, attitudes, and behaviors, particularly in the realm of technology acceptance. By adopting a cross-cultural lens, this research explores the potential variations across Chinese and international students from diverse countries in terms of attitudes and their behavioral intentions toward AI use. With a technology acceptance model (TAM) framework, the research used a survey approach, employing questionnaires as the primary means of data collection. The data were then analyzed through structural equation modeling and descriptive statistics. A substantial discrepancy was found in the prevalence, attitudes, and behavioral intentions toward AI use between Chinese and international students. Findings further revealed a stronger effect of perceived ease of use on both attitudes and behavioral intentions among international students compared with their Chinese counterparts. Findings suggest that cultural backgrounds and prior technological exposure play intricate roles in shaping perceptions of AI technology. The study emphasizes the need for tailored educational strategies to regulate diverse cultural perspectives, provide language-specific support, and ensure user-friendly interfaces. These insights contribute to the evolving discourse on technology acceptance in higher education and offer practical implications for educators and institutions toward optimizing AI integration in pedagogical practices.
References
Abdelrady, A. H., & Akram, H. (2022). An empirical study of ClassPoint tool application in enhancing EFL students’ online learning satisfaction. Systems, 10(5), Article 154. https://doi.org/10.3390/systems10050154
Akinoso, S. O. (2023). Motivation and ICT in secondary school mathematics using unified theory of acceptance and use of technology model. Indonesian Journal of Educational Research and Technology, 3(1), 79–90. https://doi.org/10.17509/ijert.v3i1.47183
Akram, H., & Abdelrady, A. H. (2023). Application of ClassPoint tool in reducing EFL learners’ test anxiety: An empirical evidence from Saudi Arabia. Journal of Computers in Education, 10, 529–547. https://doi.org/10.1007/s40692-023-00265-z
Akram, H., Abdelrady, A. H., Al-Adwan, A. S., & Ramzan, M. (2022). Teachers’ perceptions of technology integration in teaching-learning practices: A systematic review. Frontiers in Psychology, 13, Article 920317. https://doi.org/10.1007/s40692-023-00265-z
Akram, H., Aslam, S., Saleem, A., & Parveen, K. (2021). The challenges of online teaching in COVID-19 pandemic: A case study of public universities in Karachi, Pakistan. Journal of Information Technology Education. Research, 20, 263–282. https://doi.org/10.28945/4784
Akram, H., Kamran, M., & Ahmad, N. (2020). An examination of the encountered challenges of Pakistani international students in China: A case study of first-year students. Pakistan Journal of Social Sciences, 40(4), 1567–1576. https://pjss.bzu.edu.pk/index.php/pjss/article/view/959
Akram, H., & Yang, Y. (2021). A critical analysis of the weak implementation causes on educational policies in Pakistan. International Journal of Humanities and Innovation (IJHI), 4(1), 25-28. https://doi.org/10.33750/ijhi.v4i1.104
Akram, H., Yingxiu, Y., Al-Adwan, A. S., & Alkhalifah, A. (2021). Technology integration in higher education during COVID-19: An assessment of online teaching competencies through technological pedagogical content knowledge model. Frontiers in Psychology, 12, Article 736522. https://doi.org/10.3389/fpsyg.2021.736522
Akram, H., Yingxiu, Y., Aslam, S., & Umar, M. (2021b, June). Analysis of synchronous and asynchronous approaches in students' online learning satisfaction during Covid-19 pandemic. In 2021 IEEE International Conference on Educational Technology (ICET) (pp. 203-207). IEEE. https://doi.org/10.1109/ICET52293.2021.9563183
Al-Adwan, A. S., Nofal, M., Akram, H., Albelbisi, N. A., & Al-Okaily, M. (2022). Towards a sustainable adoption of e-learning systems: The role of self-directed learning. Journal of Information Technology Education: Research, 21, 245–267. https://doi.org/10.28945/4980
Alam, A. (2021, November 26–27). Possibilities and apprehensions in the landscape of artificial intelligence in education. In 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) (pp. 7–14). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCICA52458.2021.9697272
Alavi, M., Visentin, D. C., Thapa, D. K., Hunt, G. E., Watson, R., & Cleary, M. (2020). Chi-square for model fit in confirmatory factor analysis. Journal of Advanced Nursing, 76(9), 2209–2211. https://doi.org/10.1111/jan.14399
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. https://doi.org/10.1007/s10936-020-09752-1
Al-Hattami, H. M. (2023). Understanding perceptions of academics toward technology acceptance in accounting education. Heliyon, 9(1), Article E13141. https://doi.org/10.1016/j.heliyon.2023.e13141
Almaiah, M. A., Hajjej, F., Lutfi, A., Al-Khasawneh, A., Shehab, R., Al-Otaibi, S., & Alrawad, M. (2022). Explaining the factors affecting students’ attitudes to using online learning (Madrasati platform) during COVID-19. Electronics, 11(7), Article 973. https://doi.org/10.3390/electronics11070973
Al-Mamary, Y. H. S. (2022). Why do students adopt and use learning management systems? Insights from Saudi Arabia. International Journal of Information Management Data Insights, 2(2), Article 100088. https://doi.org/10.1016/j.jjimei.2022.100088
Anthony, B., Kamaludin, A., Romli, A., Raffei, A. F. M., Phon, D. N. A. E., Abdullah, A., & Ming, G. L. (2022). Blended learning adoption and implementation in higher education: A theoretical and systematic review. Technology, Knowledge and Learning, 27, 531–578. https://doi.org/10.1007/s10758-020-09477-z
Barrett, A., Pack, A., Guo, Y., & Wang, N. (2023). Technology acceptance model and multi-user virtual reality learning environments for Chinese language education. Interactive Learning Environments, 31(3), 1665–1682. https://doi.org/10.1080/10494820.2020.1855209
Breitsohl, H. (2019). Beyond ANOVA: An introduction to structural equation models for experimental designs. Organizational Research Methods, 22(3), 649–677. https://doi.org/10.1177/1094428118754988
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), Article 38. https://doi.org/10.1186/s41239-023-00408-3
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100002. https://doi.org/10.1016/j.caeai.2020.100002
Cheng, E. C., & Wang, T. (2022). Institutional strategies for cybersecurity in higher education institutions. Information, 13(4), Article 192. https://doi.org/10.3390/info13040192
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), Article 22. https://doi.org/10.1186/s41239-023-00392-8
Dai, K., & Hardy, I. (2023). Pursuing doctoral research in an emerging knowledge hub: An exploration of international students’ experiences in China. Studies in Higher Education, 48(6), 784–796. https://doi.org/10.1080/03075079.2023.2166917
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Demir, S. (2022). Comparison of normality tests in terms of sample sizes under different skewness and Kurtosis coefficients. International Journal of Assessment Tools in Education, 9(2), 397–409. https://doi.org/10.21449/ijate.1101295
Elfeky, A. I. M., & Elbyaly, M. Y. H. (2023). The use of data analytics technique in learning management system to develop fashion design skills and technology acceptance. Interactive Learning Environments, 31(6), 3810–3827. https://doi.org/10.1080/10494820.2021.1943688
Emerson, R. W. (2021). Convenience sampling revisited: Embracing its limitations through thoughtful study design. Journal of Visual Impairment & Blindness, 115(1), 76–77. https://doi.org/10.1177/0145482X20987707
Epstein, J., Santo, R. M., & Guillemin, F. (2015). A review of guidelines for cross-cultural adaptation of questionnaires could not bring out a consensus. Journal of Clinical Epidemiology, 68(4), 435–441. https://doi.org/10.1016/j.jclinepi.2014.11.021
Ertel, W. (2018). Introduction to artificial intelligence. Springer. https://doi.org/10.1007/978-3-319-58487-4
Galloway, N., & Ruegg, R. (2020). The provision of student support on English medium instruction programmes in Japan and China. Journal of English for Academic Purposes, 45, Article 100846. https://doi.org/10.1016/j.jeap.2020.100846
Granić, A. (2022). Educational technology adoption: A systematic review. Education and Information Technologies, 27(7), 9725–9744. https://doi.org/10.1007/s10639-022-10951-7
Guo, Y., Wang, Y., & Ortega-Martín, J. (2023). The impact of blended learning-based scaffolding techniques on learners’ self-efficacy and willingness to communicate. Porta Linguarum, 40(2), 253–273. https://doi.org/10.30827/portalin.vi40.27061
Hair, J., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), Article 100027. https://doi.org/10.1016/j.rmal.2022.100027
Huang, F., Teo, T., & Zhao, X. (2023). Examining factors influencing Chinese ethnic minority English teachers’ technology adoption: An extension of the UTAUT model. Computer Assisted Language Learning. Advance online publication. https://doi.org/10.1080/09588221.2023.2239304
Jiani, M. A. (2017). Why and how international students choose mainland China as a higher education study abroad destination. Higher Education, 74, 563–579. https://doi.org/10.1007/s10734-016-0066-0
Kayalar, F. (2016). Cross-cultural comparison of teachers’ views upon integration and use of technology in classroom. Turkish Online Journal of Educational Technology-TOJET, 15(2), 11–19. https://eric.ed.gov/?id=EJ1096412
Kim, S. W., & Lee, Y. (2023). Investigation into the influence of socio-cultural factors on attitudes toward artificial intelligence. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-023-12172-y
Lai, M. H. (2021). Composite reliability of multilevel data: It’s about observed scores and construct meanings. Psychological Methods, 26(1), 90–102. https://doi.org/10.1037/met0000287
Lasfeto, D. B., & Ulfa, S. (2023). Modeling of online learning strategies based on fuzzy expert systems and self-directed learning readiness: The effect on learning outcomes. Journal of Educational Computing Research, 60(8), 2081–2104. https://doi.org/10.1177/07356331221094249
Lee, C. (2022). Intention to use versus actual adoption of technology by university English language learners: What perceptions and factors matter? Computer Assisted Language Learning, 35(8), 2049–2077. https://doi.org/10.1080/09588221.2020.1857410
Lewis, J. R. (2019). Comparison of four TAM item formats: Effect of response option labels and order. Journal of User Experience, 14(4), 224–236. https://uxpajournal.org/tam-formats-effect-response-labels-order/
Linardatos, G., & Apostolou, D. (2023). Investigating high school students’ perception about digital comics creation in the classroom. Education and Information Technologies, 28, 10079–10101. https://doi.org/10.1007/s10639-023-11581-3
Makumane, M. A. (2023). Students’ perceptions on the use of LMS at a Lesotho university amidst the COVID-19 pandemic. African Identities, 21(2), 209–226. https://doi.org/10.1080/14725843.2021.1898930
Matore, E. M., & Khairani, A. Z. (2020). The pattern of skewness and kurtosis using mean score and logit in measuring adversity quotient (AQ) for normality testing. International Journal of Future Generation Communication and Networking, 13(1), 688–702. http://sersc.org/journals/index.php/IJFGCN/article/view/6571
Matsika, C., & Zhou, M. (2021). Factors affecting the adoption and use of AVR technology in higher and tertiary education. Technology in Society, 67, Article 101694. https://doi.org/10.1016/j.techsoc.2021.101694
Min, Y., Yang, M., Huang, J., & Duangekanong, S. (2023). Influencing factors of behavior intention of Master of Arts students towards online education in Chengdu public universities, China. Scholar: Human Sciences, 15(1), 1–10. https://doi.org/10.14456/shserj.2023.1
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, Article 100020. https://doi.org/10.1016/j.caeai.2021.100020
Papakostas, C., Troussas, C., Krouska, A., & Sgouropoulou, C. (2023). Exploring users’ behavioral intention to adopt mobile augmented reality in education through an extended technology acceptance model. International Journal of Human–Computer Interaction, 39(6), 1294–1302. https://doi.org/10.1080/10447318.2022.2062551
Park, D. Y., & Kim, H. (2023). Determinants of intentions to use digital mental healthcare content among university students, faculty, and staff: Motivation, perceived usefulness, perceived ease of use, and parasocial interaction with AI chatbot. Sustainability, 15(1), Article 872. https://doi.org/10.3390/su15010872
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 587–632). Springer International Publishing. https://doi.org/10.1007/978-3-319-57413-4_15
Sathye, S., Prasad, B., Sharma, D., Sharma, P., & Sathye, M. (2018). Factors influencing the intention to use of mobile value‐added services by women‐owned microenterprises in F iji. The Electronic Journal of Information Systems in Developing Countries, 84(2), e12016. https://doi.org/10.1002/isd2.12016
Savicki, V. (Ed.). (2023). Developing intercultural competence and transformation: Theory, research, and application in international education. Routledge. https://doi.org/10.4324/9781003444169
Steyn, C., & Gunter, A. (2023). When an international student stays at home: Defining an international student in distance education. Journal of Geography in Higher Education, 47(1), 56–70. https://doi.org/10.1080/03098265.2021.1991289
Sutrisno, S., & Lubis, A. H. (2022). A survey on online computer practicum during the COVID-19 pandemic: Students’ perceptions. International Journal of Research and Review, 9(9), 372–379. https://doi.org/10.52403/ijrr.20220942
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
Taherdoost, H. (2016). Validity and reliability of the research instrument; How to test the validation of a questionnaire/survey in a research. International Journal of Academic Research in Management (IJARM), 5(3), 28–36. https://doi.org/10.2139/ssrn.3205040
Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers’ attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89–109. https://doi.org/10.2190/EC.40.1.d
Vargo, D., Zhu, L., Benwell, B., & Yan, Z. (2021). Digital technology use during COVID‐19 pandemic: A rapid review. Human Behavior and Emerging Technologies, 3(1), 13–24. https://doi.org/10.1002/hbe2.242
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wang, J., & Wang, X. (2019). Structural equation modeling: Applications using Mplus. John Wiley & Sons. https://doi.org/10.1002/9781119422730
Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023). Exploring the potential impact of artificial intelligence (AI) on international students in higher education: Generative AI, chatbots, analytics, and international student success. Applied Sciences, 13(11), Article 6716. https://doi.org/10.3390/app13116716
Wang, Y. (2023). Probing into the boredom of online instruction among Chinese English language teachers during the COVID-19 pandemic. Current Psychology, 43, 12144–12158. https://doi.org/10.1007/s12144-022-04223-3
Wang, Y. A., & Rhemtulla, M. (2021). Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial. Advances in Methods and Practices in Psychological Science, 4(1). https://doi.org/10.1177/2515245920918253
Xiao, J., & Goulias, K. G. (2022). Perceived usefulness and intentions to adopt autonomous vehicles. Transportation Research Part A: Policy and Practice, 161, 170–185. https://doi.org/10.1016/j.tra.2022.05.007
Xiong, W., Yang, J., & Shen, W. (2022). Higher education reform in China: A comprehensive review of policymaking, implementation, and outcomes since 1978. China Economic Review, 72, Article 101752. https://doi.org/10.1016/j.chieco.2022.101752
Yang, F., Ren, L., & Gu, C. (2022). A study of college students’ intention to use metaverse technology for basketball learning based on UTAUT2. Heliyon, 8(9), Article E10562. https://doi.org/10.1016/j.heliyon.2022.e10562
Yilmaz, R., & Yilmaz, F. G. K. (2023). The effect of generative artificial intelligence (AI)-based tool use on students’ computational thinking skills, programming self-efficacy and motivation. Computers and Education: Artificial Intelligence, 4, Article 100147. https://doi.org/10.1016/j.caeai.2023.100147
Zhang, Y., & Zhu, Y. (2022). Effects of educational robotics on the creativity and problem-solving skills of K-12 students: A meta-analysis. Educational Studies. Advance online publication. https://doi.org/10.1080/03055698.2022.2107873
Zhou, C. (2023). Integration of modern technologies in higher education on the example of artificial intelligence use. Education and Information Technologies, 28(4), 3893–3910. https://doi.org/10.1007/s10639-022-11309-9
Zulaiha, D., & Triana, Y. (2023). Students’ perception toward the use of open educational resources to improve writing skills. Studies in English Language and Education, 10(1), 174–196. https://doi.org/10.24815/siele.v10i1.25797
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International Licence. The copyright of all content published in IRRODL is retained by the authors.
This copyright agreement and use license ensures, among other things, that an article will be as widely distributed as possible and that the article can be included in any scientific and/or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
