Understanding AI Adoption In Education: The Role of Readiness, Confidence, And Social Influence Among Pakistani Students

Asad Ur Rehman (1) , Muhammad Ali  Raza (2) , Nasir Abbas (3)
1. Department of Commerce Bahauddin Zakariya University, Multan, Punjab, Pakistan
2. Department of Commerce, Bahauddin Zakriya University, Multan, Punjab, Pakistan
3. Government College University Faisalabad

Abstract

The purpose of this study is to explore the key factors influencing Artificial Intelligence (AI) adoption in education among Pakistani university students. Specifically, it examines how AI Readiness (AIRD), AI Confidence (AICF), and Social Influence (SI) affect students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU), and how these perceptions shape their Attitudes toward AI (ATT). The study also investigates the mediating roles of PEOU and PU. A quantitative research design was adopted using survey data collected from Pakistani students. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied through Smart PLS 4 to assess both the measurement and structural models. The results reveal that AIRD, AICF, and SI significantly influence students’ perceptions of ease of use, while AIRD and SI also positively impact perceived usefulness. However, AI confidence does not appear to shape perceived usefulness. Notably, perceived ease of use plays a substantial role in forming positive attitudes toward AI, while perceived usefulness does not have a direct effect. Mediation analysis further confirms that PEOU mediates the relationship between AIRD, AICF, SI, and ATT, whereas PU does not. The findings underscore the critical importance of usability over perceived benefits in shaping students' acceptance of AI technologies. In contexts where AI adoption is still emerging, ease of use appears to be the dominant factor influencing attitudes. Educators and policymakers should focus on enhancing students’ readiness and confidence in using AI, promoting user-friendly tools, and leveraging social influence to drive adoption. These insights are crucial for designing inclusive strategies that support effective AI integration into educational environments.

Full text article

Generated from XML file

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 Google Scholar | Crossref | WorldCat

Abulkassova, D., Muldasheva, G., Nurtazin, M., Tleukhanov, N., & Kuspanova, A. (2025). The phenomenon of artificial intelligence in modern transformational socio-cultural processes: Socio-philosophical analysis. AI and Society. https://doi.org/10.1007/s00146-025-02195-z Google Scholar | Crossref | WorldCat

Aijaz, U., Lodhi, K. S., Shamim, M. A., & Mughal, S. (2024). Economics of education and digital learning for human capital development in Pakistan: A critical review. Qlantic Journal of Social Sciences, 5(1), 217–234. https://doi.org/10.55737/qjss.349367331 Google Scholar | Crossref | WorldCat

Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs. Google Scholar | WorldCat

Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T Google Scholar | Crossref | WorldCat

Al Yakin, A., Rasyid, A. R., Massyat, M., Muthmainnah, M., Aquino, A. B., Noche, T. H., & Cardoso, L. (2024). The importance of collaboration between human intelligence and GenAI in the digitalization of education. In Impact and Potential of Machine Learning in the Metaverse (pp. 129–160). IGI Global. https://doi.org/10.4018/979-8-3693-5762-0.ch006 Google Scholar | Crossref | WorldCat

Ali Shah, M. H., Dharejo, N., Mahar, Z. A., Nazeer, M. I., Khoso, I. A., & Shah, A. (2024). Significant predictors influencing the adoption of ChatGPT usage in academia in Sindh, Pakistan: Extension of the UTAUT model. Pegem Journal of Education & Instruction, 14(4). https://doi.org/10.47750/pegegog.14.04.14 Google Scholar | Crossref | WorldCat

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall. Google Scholar | WorldCat

Bhimavarapu, A., Thiyyagura, D., Pasupuleti, R. S., & Babu, P. V. (2024). Artificial intelligence in the pocket: Factors influencing Generation Z's intention to use AI-powered mobile banking applications. 2024 1st International Conference on Data, Computation and Communication (ICDCC), 278–283. https://doi.org/10.1109/ICDCC62744.2024.10961558 Google Scholar | Crossref | WorldCat

Bond, M. (2024). The International Journal of Educational Technology in Higher Education: Content and authorship analysis 2010–2024. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00492-z Google Scholar | Crossref | WorldCat

Cao, H. J., Choi, K., Park, C., & Lee, H. R. (2025). AI literacy for underserved students: Leveraging cultural capital from underserved communities for AI education research. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3706598.3713173 Google Scholar | Crossref | WorldCat

Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118 Google Scholar | Crossref | WorldCat

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688 Google Scholar | Crossref | WorldCat

Crompton, H., & Traxler, J. (2018). Mobile learning and higher education: Challenges in context. Routledge. Google Scholar | WorldCat

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 Google Scholar | Crossref | WorldCat

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley. Google Scholar | WorldCat

Geddam, S. M., Nethravathi, N., & Hussian, A. A. (2024). Understanding AI adoption: The mediating role of attitude in user acceptance. Journal of Informatics Education and Research, 4(2). https://doi.org/10.52783/jier.v4i2.975 Google Scholar | Crossref | WorldCat

Hair, J. F., Matthews, L. M., Matthews, R. L., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624 Google Scholar | Crossref | WorldCat

Holmes, W. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. Google Scholar | WorldCat

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 Google Scholar | Crossref | WorldCat

Hussain, M. M., Hanif, S., Ghauri, K., & ul Ain, Q. (2025). The role of behavioral intention in AI adoption and student success in higher education institutions: A UTAUT2 perspective. Indus Journal of Social Sciences, 3(2), 341–357. https://doi.org/10.59075/ijss.v3i2.1221 Google Scholar | Crossref | WorldCat

Hussain, S., Rehman, S. ur, Rasheed, A., & Rehman, K. ur. (2025). Enhancing competitiveness in Pakistan: The role of green product innovation performance, artificial intelligence adoption, and government involvement in business strategies. Journal of the Knowledge Economy. Advance online publication. https://doi.org/10.1007/s13132-025-02696-8 Google Scholar | Crossref | WorldCat

Khurshid, S., Khurshid, S., & Toor, H. K. (2024). Learning for an uncertain future: Artificial intelligence a challenge for Pakistani education system in the era of digital transformation. Qualitative Research Journal. Advance online publication. https://doi.org/10.1108/QRJ-02-2024-0045 Google Scholar | Crossref | WorldCat

Kufaine, N. (2024). Understanding characteristics of extended theory of planned behaviour: Systematic literature review. Open Journal of Philosophy, 14(4), 848–858. https://doi.org/10.4236/ojpp.2024.144057 Google Scholar | Crossref | WorldCat

Pillai, S., & Ramakrishnan, R. (2024). AI in education: Balancing innovation and responsibility. In C. Goncalves & J. C. D. Rouco (Eds.), Proceedings of the 4th International Conference on AI Research (ICAIR 2024) (pp. 345–354). Academic Conferences International Limited. Google Scholar | WorldCat

Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 22. https://doi.org/10.1186/s41039-017-0062-8 Google Scholar | Crossref | WorldCat

Sarstedt, M., Hair, J. F., Pick, M., Liengaard, B. D., Radomir, L., & Ringle, C. M. (2022). Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology & Marketing, 39(5), 1035–1064. https://doi.org/10.1002/mar.21640 Google Scholar | Crossref | WorldCat

Soares, A., Lerigo-Sampson, M., & Barker, J. (2025). Recontextualising the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to higher education online marking. Journal of University Teaching and Learning Practice, 21(8), 1–26. https://doi.org/10.53761/7ft8x880 Google Scholar | Crossref | WorldCat

Syed, A. Z., Memon, Z. H., Khan, K., Hameed, I., & Nadeem, M. (2025). Examining the behavioral determinants of AI adoption in higher education: A focus on perceptional factors and demographic differences. On the Horizon. Advance online publication. https://doi.org/10.1108/OTH-02-2025-0019 Google Scholar | Crossref | WorldCat

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 Google Scholar | Crossref | WorldCat

Wong, P. P. Y., Lee, J., Gonzales, W. D. W., Choi, S. H. S., Hwang, H., & Shen, D. J. (2024). New dimensions: The impact of the metaverse and AI avatars on social science education. In W. W. K. Ma et al. (Eds.), Lecture Notes in Computer Science (Vol. 14797, pp. 90–101). Springer. https://doi.org/10.1007/978-981-97-4442-8_7 Google Scholar | Crossref | WorldCat

Zaman, S. U., Ali, S. S., Alam, S. H., & Kamal, M. H. (2025). Assessing students' behavioral intentions towards AI-based learning tools. Journal of Asian Development Studies, 14(1), 656–672. https://doi.org/10.62345/jads.2025.14.1.50 Google Scholar | Crossref | WorldCat

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0 Google Scholar | Crossref | WorldCat

Authors

Asad Ur Rehman
asadrehmaan@bzu.edu.pk (Primary Contact)
Author Biographies

Asad Ur Rehman

Dr. Asad Ur Rehman is an Associate Professor in the Department of Commerce at Bahauddin Zakariya University (BZU), Multan, Punjab, Pakistan. With over 1,600 citations and an h-index of 21, his research primarily focuses on marketing and consumer behavior in emerging economies. Dr. Rehman has contributed significantly to understanding branding dynamics and customer satisfaction in local contexts. He holds a PhD in Marketing and actively mentors postgraduate students while teaching undergraduate and graduate courses.

Muhammad Ali  Raza

Muhammad Ali Raza is a PhD Scholar in the Department of Commerce at Bahauddin Zakariya University, Multan. His research interests encompass entrepreneurship, sustainability, and marketing, and he has authored scholarly work on business-to-customer relationships and Islamic banking profitability. As a doctoral candidate, he is developing his expertise through empirical research to support Pakistan’s academic and commercial sectors.

Nasir Abbas

Nasir Abbas is a Lecturer in the Department of Commerce at Government College University Faisalabad (GCUF). He holds a PhD in Accounting and Finance and has published research on technical efficiency in agriculture and green finance’s impact on corporate sustainability. With experience in quantitative methods and survey-based studies, he brings valuable academic and teaching contributions to the department.

Rehman , A. U., Raza, M., & Abbas, N. (2025). Understanding AI Adoption In Education: The Role of Readiness, Confidence, And Social Influence Among Pakistani Students. Innovation Journal of Social Sciences and Economic Review, 7(1), 64-78. https://doi.org/10.36923/ijsser.v7i1.300

Article Details

How to Cite

Rehman , A. U., Raza, M., & Abbas, N. (2025). Understanding AI Adoption In Education: The Role of Readiness, Confidence, And Social Influence Among Pakistani Students. Innovation Journal of Social Sciences and Economic Review, 7(1), 64-78. https://doi.org/10.36923/ijsser.v7i1.300

Similar Articles

You may also start an advanced similarity search for this article.