Bridging the Gap: How AI Literacy Shapes Perceptions of Usefulness, Ease of Use, and Risks in AI Adoption among University Students

Authors

  • Muhammad Mohsin Khan Lecturer, Department of Sociology, University of Sargodha, Sargodha, Punjab, Pakistan.
  • Mihammad Arslan BS Scholar, Department of Sociology, University of Sargodha, Sargodha, Punjab, Pakistan.  
  • Khalil Haider BS Scholar, Department of Sociology, University of Sargodha, Sargodha, Punjab, Pakistan.
  • Muhammad Arif BS Scholar, Department of Sociology, University of Sargodha, Sargodha, Punjab, Pakistan.
  • Batool Fatima BS Scholar, Department of Sociology, University of Sargodha, Sargodha, Punjab, Pakistan.

DOI:

https://doi.org/10.55737/rl.v5i1.26166

Keywords:

ChatGPT, Artificial Intelligence, Perceived Usefulness, Perceived Ease, Perceived Risk, AI Literacy

Abstract

The rapid integration of artificial intelligence tools, especially ChatGPT, into educational settings has generated heated discussions among educators, students, and policymakers. This study explores the University students' attitudes towards the use of ChatGPT in education by examining the impact of perceived usefulness (PU), perceived ease of use (PEOU), and perceived risks (PR) on users' overall perception (OP) while AI literacy (AIL) is considered a moderating variable. Through a quantitative research design based on the Technology Acceptance Model (TAM), this study surveys the university students enrolled in different degree levels and fields of study to gain insights into their attitudes, behaviors, and concerns about the implementation of ChatGPT in educational contexts. Smart PLS (SEM) was utilized to validate the hypotheses. The research findings revealed that perceived usefulness, ease of use, and perceived risks are three major factors that have significantly changed users' overall perception of AI. Nevertheless, AI literacy was not a significant moderator of these relationships, indicating that people's perceptions are mainly determined by the features of the technology rather than the level of user literacy. The results add value to the existing literature on the application of AI in education and provide practical implications for educational institutions that aim to develop policies and guidelines for responsible use of AI tools. This research, on one hand, explores the virtual literacy component and, on the other, it delves into the functioning of the balance between technological innovation and educational integrity.

Author Biography

  • Mihammad Arslan, BS Scholar, Department of Sociology, University of Sargodha, Sargodha, Punjab, Pakistan.  

    Corresponding Author: [email protected]

References

Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for E-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036

Al-Sharafi, M. A., Al-Qaysi, N., Iahad, N. A., & Al-Emran, M. (2021). Evaluating the sustainable use of mobile payment contactless technologies within and beyond the COVID-19 pandemic using a hybrid SEM-ANN approach. International Journal of Bank Marketing, 40(5), 1071-1095. https://doi.org/10.1108/ijbm-07-2021-0291

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4337484

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … & Liang, P. (2021). On the opportunities and risks of foundation models (arXiv:2108.07258) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2108.07258

Borenstein, J., & Howard, A. (2020). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1), 61-65. https://doi.org/10.1007/s43681-020-00002-7

Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T.J., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., & Amodei, D. (2020). Language Models are Few-Shot Learners. ArXiv, abs/2005.14165. https://doi.org/10.48550/arXiv.2005.14165

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), Article 43. https://doi.org/10.1186/s41239 023 00411 8

Cotton, D., Cotton, P., & Shipway, J. R. (2023). Chatting and cheating. Ensuring academic integrity in the era of ChatGPT. https://doi.org/10.35542/osf.io/mrz8h

Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20, 1-22. https://doi.org/10.1186/s41239-023-00392-8

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340.

https://doi.org/10.2307/249008

Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., Baabdullah, A.M., Koohang, A., Raghavan, V., Ahuja, M.K., Albanna, H., Albashrawi, M.A., Al-Busaidi, A.S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L.D., Buhalis, D., Carter, L.D., Chowdhury, S., Crick, T., Cunningham, S.W., Davies, G.H., Davison, R.M., De', R., Dennehy, D., Duan, Y., Dubey, R., Dwivedi, R., Edwards, J.S., Flavián, C., Gauld, R., Grover, V., Hu, M., Janssen, M., Jones, P.C., Junglas, I.A., Khorana, S., Kraus, S., Larsen, K.R., Latreille, P., Laumer, S., Malik, F.T., Mardani, A., Mariani, M.M., Mithas, S., Mogaji, E., Nord, J.H., O'Connor, S., Okumus, F., Pagani, M., Pandey, N., Papagiannidis, S., Pappas, I.O., Pathak, N., Pries-Heje, J., Raman, R., Rana, N.P., Rehm, S., Ribeiro-Navarrete, S., Richter, A., Rowe, F., Sarker, S., Stahl, B.C., Tiwari, M.K., van der Aalst, W.M., Venkatesh, V., Viglia, G., Wade, M., Walton, P., Wirtz, J., & Wright, R.D. (2023). Opinion Paper: "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manag., 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Featherman, M.S. and Pavlou, P.A. (2003) Predicting e-Services Adoption: A Perceived Risk Facets Perspective. International Journal of Human-Computer Studies, 59, 451-474.

http://dx.doi.org/10.1016/S1071-5819(03)00111-3

Field, A. P. (2018). Discovering Statistics Using IBM SPSS Statistics. 5th Edition, Sage, Newbury Park.

Hair, J. F., & Alamer, A. (2023). 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

Holmes, W., Bialik, M., and Fadel, C. (2019). Artificial intelligence in training: Promises and implications for coaching and getting to know. Center for Curriculum Redesign.

Hu, X., Tian, Y., Nagato, K., Nakao, M., & Liu, A. (2023). Opportunities and challenges of ChatGPT for design knowledge management. Procedia CIRP, 119, 21-28. https://doi.org/10.1016/j.procir.2023.05.001

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G.L., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences. https://doi.org/10.1016/j.lindif.2023.102274

Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and Education: Artificial Intelligence, 3, 100074. https://doi.org/10.1016/j.caeai.2022.100074

Lo, C. K. (2023). What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410

Long, D., & Magerko, B. (2020). What Is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). Association for Computing Machinery.https://doi.org/10.1145/3313831.3376727

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

Ng, D. T. K., Su, J., & Chu, S. K. W. (2024). Fostering secondary school students’ AI literacy through making AI-driven recycling bins. Education and Information Technologies, 29(8), 9715–9746. https://doi.org/10.1007/s10639-023-12183-9

OpenAI (2023). ChatGPT (Mar 14 Version) [Large Language Model].

https://chat.openai.com/chat

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback (arXiv:2203.02155) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2203.02155

Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2). https://doi.org/10.53761/1.20.02.07

Prensky, M. (2001). Digital Natives, Digital Immigrants Part 1. on The Horizon, 9, 1-6.

Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit Spewer or the End of Traditional Assessments in Higher Education? Journal of Applied Learning and Teaching, 6, 342-363.

https://doi.org/10.37074/jalt.2023.6.1.9

Šumak, B., Heričko, M., & Pušnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27(6), 2067–2077. https://doi.org/10.1016/j.chb.2011.08.005

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), Article 15. https://doi.org/10.1186/s40561 023 00237

Weber Wulff, D., Anohina Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI generated text. International Journal for Educational Integrity, 19(26), 1–39. https://doi.org/10.1007/s40979 023 00146 z

Downloads

Published

2026-02-09

How to Cite

Khan, M. M., Arslan, M., Haider, K., Arif, M., & Fatima, B. (2026). Bridging the Gap: How AI Literacy Shapes Perceptions of Usefulness, Ease of Use, and Risks in AI Adoption among University Students. Regional Lens, 5(1), 81-91. https://doi.org/10.55737/rl.v5i1.26166