Exploring The Role of Digital Literacy in University Students' Engagement with AI through the Technology Acceptance Model

Abstract

Through the last decades, Artificial Intelligence (AI) has revolutionized the field of education and transformed traditional teaching approaches. This study aimed to examine how university students adopt AI tools in their learning processes and the role of digital literacy (DL) in this process through the lens of the Technology Acceptance Model (TAM). In this context, this study measured the impact of DL on university students' acceptance of AI technologies and their intention to use such technologies in the future. The data was collected from university students (N = 154) at a university in Western Türkiye during the fall semester of 2023. Data collection was conducted using two separate online forms; the first form included items adapted from the Digital Literacy Scale developed by Bayrakçı and Narmanlıoğlu (2021) to measure digital literacy levels, while the second form included items adapted from the UTAUT study by Venkatesh et al. (2003). The hypothesis testing results showed that students with higher levels of DL perceived the usefulness and ease of use of AI tools more positively, which positively affected their intention to adopt AI-based tools. The study also found that perceived usefulness and ease of use were important in shaping students' attitudes and behavioural intentions towards AI. When students perceive AI as a valuable tool for learning and find it easy to interact with, they are more willing to use it. This study suggests that DL plays a significant role in the acceptance of AI-based tools among university students, and accordingly, the TAM is a practical and accurate model to explore students’ potential engagement with AI in the learning process.

Keywords

• Digital Literacy
• Artificial Intelligence
• Technology Acceptance Model
• University Students

References

1. Aba Shaar, M. Y. M., Buddharat, C., & Singhasuwan, P. (2022). Enhancing students’ English and digital literacies through online courses: Benefits and challenges. Turkish Online Journal of Distance Education, 23 (3), 154–178. https://doi.org/10.17718/tojde.1137256
2. Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspective. Computers and Education: Artificial Intelligence, 4, 100132. https://doi.org/10.1016/j.caeai.2023.100132
3. Alakrash, H. M., & Razak, N. A. (2021). Technology-based language learning: investigation of digital technology and digital literacy. In Sustainability, 13(21),1-17. https://doi.org/10.3390/su132112304
4. Alhashmi, S. F., Salloum, S. A., & Mhamdi, C. (2019). Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector: An Extended Technology Acceptance Model. International Journal of Information Technology and Language Studies, 3(3), 27-42. Retrieved from https://journals.sfu.ca/ijitls/index.php/ijitls/article/view/107
5. Alzahrani, L. (2023). Analyzing students’ attitudes and behavior toward artificial intelligence technologies in higher education. International Journal of Recent Technology and Engineering (IJRTE), 11(6), 65–73. https://doi.org/10.35940/ijrte.F7475.0311623
6. Aslan, S. (2021). Analysis of digital literacy self-efficacy levels of pre-service teachers. International Journal of Technology in Education, 4(1), 57–67. https://doi.org/10.46328/ijte.47
7. Bacalja, A., Beavis, C., & O’Brien, A. (2022). Shifting landscapes of digital literacy. Australian Journal of Language and Literacy, 45(2), 253-263 https://doi.org/10.10071/s44020-022-00019-x
8. Bagozzi, R. P., & Phillips, L. W. (1982). Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly, 27(3), 459–489. https://doi.org/10.2307/2392322

9. Bayrakci, S., & Narmanlioğlu, H. (2021). Digital literacy as whole of digital competences: scale development study. Düşünce ve Toplum Sosyal Bilimler Dergisi, 4, 1-30. Retrieved from https://dergipark.org.tr/en/pub/dusuncevetoplum/issue/63163/945319
10. Bulganina, S., V., Prokhorova, M. P., Lebedeva, T. E., Shkunova, A. A., & Mikhailov, M. S. (2021). Digital skills as a response to the challenges of the modern society. Turismo-Estudos E Praticas, 1, 1-7. Retrieved from https://geplat.com/rtep/index.php/tourism/article/view/878
11. Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education, 30(2), 107–130. https://doi.org/10.1080/09639284.2021.1872035
12. 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
13. Edmunds, R., Thorpe, M., & Conole, G. (2012). Student attitudes towards and use of ICT in course study, work and social activity: A technology acceptance model approach. British Journal of Educational Technology, 43(1), 71–84. https://doi.org/10.1111/j.1467-8535.2010.01142.x
14. Gherheș, V., & Obrad, C. (2018). Technical and humanities students’ perspectives on the development and sustainability of artificial intelligence (AI). Sustainability, 10(9), 3066. https://doi.org/10.3390/su10093066
15. Gie, T., & Chung, J. F. (2019). Technology acceptance model and digital literacy of first-year students in a private institution of higher learning in Malaysia. BERJAYA Journal of Services & Management, 11, 103-116. https://doi.org/10.5281/zenodo.2622329
16. Gutierrez-Angel, N., Sanchez-Garcia, J.-N., Mercader-Rubio, I., Garcia-Martin, J., & Brito-Costa, S. (2022). Digital literacy in the university setting: A literature review of empirical studies between 2010 and 2021. Frontiers in Psychology, 13, 1-35. https://doi.org/10.3389/fpsyg.2022.896800
17. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis (4th ed.): With readings. Prentice-Hall, Inc.
18. Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424–453. https://doi.org/10.1037/1082-989X.3.4.424
19. Jan, A. U., & Contreras, V. (2011). Technology acceptance model for the use of information technology in universities. Computers in Human Behavior, 27(2), 845–851. https://doi.org/10.1016/j.chb.2010.11.009
20. Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1–19. https://doi.org/10.1108/IJILT-05-2020-0090
21. Khlaisang, J., & Yoshida, M. (2022). Empowering global citizens with digital literacy: odeling the factor structure. International Journal of Instruction, 15(4), 577–594. https://doi.org/10.29333/iji.2022.15431a
22. Ko, Y.-H., & Leem, C.-S. (2021). The influence of ai technology acceptance and ethical awareness towards intention to use. journal of digital convergence, 19(3), 217–225. https://doi.org/10.14400/JDC.2021.19.3.217
23. Koppel, I., & Langer, S. (2020). Basic digital literacy – requirements and elements. Práxis Educacional, 16(42), 326–347. https://doi.org/10.22481/praxisedu.v16i42.7354
24. Kumar Kakar, A. (2017). How do perceived enjoyment and perceived usefulness of a software product interact over time to impact technology acceptance? Interacting with Computers, 29(4), 467–480. https://doi.org/10.1093/iwc/iwx006
25. 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
26. Liu, G. (2023). To transform or not to transform? Understanding the digital literacies of rural lower-class efl learners. Journal of Language Identity and Education, 1-18. https://doi.org/10.1080/15348458.2023.2236217
27. Mac Callum, K., Jeffrey, L., & Na, K. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education: Research, 13, 141–162. https://doi.org/10.28945/1970
28. Mailizar, M., Umam, K., & Elisa, E. (2022). The impact of digital literacy and social presence on teachers’ acceptance of online professional development. Contemporary Educational Technology, 14(4), ep384. https://doi.org/10.30935/cedtech/12329
29. Mohammadyari, S., & Singh, H. (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers & Education, 82, 11–25. https://doi.org/10.1016/j.compedu.2014.10.025
30. Mohr, S., & Kuhl, R. (2021). Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816–1844. https://doi.org/10.1007/s11119-021-09814-x
31. Na, S., Heo, S., Choi, W., Kim, C., & Whang, S. W. (2023). Artificial intelligence (AI)-based technology adoption in the construction industry: A cross national perspective using the technology acceptance model. Buildings, 13(10), 2518. https://doi.org/10.3390/buildings13102518
32. Nikou, S., & Aavakare, M. (2021). An assessment of the interplay between literacy and digital Technology in Higher Education. Education and Information Technologies, 26(4), 3893–3915. https://doi.org/10.1007/s10639-021-10451-0
33. Pegalajar Palomino, M. del C., & Rodriguez Torres, Angel F. (2023). Digital literacy in university students of education degrees in Ecuador. Frontiers in Education, 8, 1-8. https://doi.org/10.3389/feduc.2023.1299059
34. Romero-Hall, E., & Cherrez, N. J. (2023). Teaching in times of disruption: Faculty digital literacy in higher education during the COVID-19 pandemic. Innovations in Education and Teaching International, 60(2), 152–162. https://doi.org/10.1080/14703297.2022.2030782
35. Schumacker, R., & Lomax, R. (1996). a beginner’s guide to structural equation modeling (2nd ed.). Psychology Press. https://doi.org/10.4324/9781410610904
36. Skehan, P. (1996). A framework for the implementation of task-based instruction. Applied Linguistics, 17(1), 38–62. https://doi.org/10.1093/applin/17.1.38
37. Smith, E. E., & Storrs, H. (2023). Digital literacies, social media, and undergraduate learning: What do students think they need to know? International Journal of Educational Technology in Higher Education, 20(1), 1-19. https://doi.org/10.1186/s41239-023-00398-2
38. Strzelecki, A. (2023). Students’ acceptance of chatgpt in higher education: an extended unified theory of acceptance and use of technology. Innovative Higher Education, 49, 223-245. https://doi.org/10.1007/s10755-023-09686-1
39. Taber, K. S. (2018). The use of cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
40. Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students: A structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124–142. https://doi.org/10.1007/s12528-014-9080-3
41. Tian, X., Park, K. H., & Liu, Q. (2023). Deep learning influences on higher education students’ digital literacy: The meditating role of higher-order thinking. International Journal of Engineering Pedagogy, 13(6), 33–49. https://doi.org/10.3991/ijep.v13i6.38177
42. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
43. 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
44. Wang, B., Rau, P.-L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
45. Yoleri, S., & Anadolu, Z. N. (2022). Examination of digital literacy skills of undergraduate students according to various variables. Advanced Education, 21, 121–134. https://doi.org/10.20535/2410-8286.262190
46. Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49. https://doi.org/10.1186/s41239-023-00420-7
47. Zou, M., & Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14. 1259531. https://doi.org/10.3389/fpsyg.2023.1259531