Understanding AI Adoption In Education: The Role of Readiness, Confidence, And Social Influence Among Pakistani Students
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
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
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
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
Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs.
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
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
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
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
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
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
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
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
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
Crompton, H., & Traxler, J. (2018). Mobile learning and higher education: Challenges in context. Routledge.
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
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
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
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
Holmes, W. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
Authors
Copyright (c) 2025 Asad Ur Rehman , Muhammad?Ali? Raza, Nasir Abbas
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright / Open Access Policy
This journal provides immediate and free open access to all its content and is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means readers are permitted to read, download, copy, distribute, print, search, or link to the full texts of articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author, as long as proper attribution is given. This policy is consistent with the Budapest Open Access Initiative (BOAI) definition of open access.
Article Details
How to Cite
Similar Articles
- Muhammad Naveed, Under Pressure to Perform: How Social Cues in Academia Shape Perfectionism Among Early-Career Scholars , Innovation Journal of Social Sciences and Economic Review: Vol. 7 No. 2 (2025)
- Hafiza Rabia Alam, Muhammad Shakir, Causes of the Passive Attitude in Children at Early Grade Level , Innovation Journal of Social Sciences and Economic Review: Vol. 1 No. 1 (2019)
- Maria Afreen, Construction of an Industry Cycle Indicator for Profitability Prediction Analysis of Aggregate Firms in Bangladesh , Innovation Journal of Social Sciences and Economic Review: Vol. 2 No. 4 (2020)
- Mamello Motaung, Hester Roberts, Pakiso Makhoahle, Environmental And Health Implications of Waste Disposal Sites In The Lesotho Lowlands , Innovation Journal of Social Sciences and Economic Review: Vol. 6 No. 2 (2024)
- Mmathato Calphurnia Rammutla, Ndivhuwo Doctor Sundani, Social Media’s Influence on the Attitudes of Younger Daughters-in-Law towards their Mothers-in-Law: A South African Context , Innovation Journal of Social Sciences and Economic Review: Vol. 6 No. 1 (2024)
- Mmathato Calphurnia Rammutla, Challenges of ICT Integration in Social Work Practicums in African Higher Education: A Systematic Review , Innovation Journal of Social Sciences and Economic Review: Vol. 6 No. 1 (2024)
- Joseph Malechwanzi, From Profit To Purpose: How Corporate Social Responsibility Transforms Rural Communities In Kenya , Innovation Journal of Social Sciences and Economic Review: Vol. 7 No. 2 (2025)
- Shimelis Wondimu, Tamirie Andualem, Marital Beliefs, Marital Virtues, and Neighborhood Cohesion as Predictors of Marital Satisfaction in Addis Ababa, Ethiopia: A Multilevel Analysis , Innovation Journal of Social Sciences and Economic Review: Vol. 6 No. 1 (2024)
- Nebeyu Yohannes Wassie, Daniel Tefera Abebe, Unveiling the Dynamics of Women's Self-Help Groups: Exploring Socio-Economic and Socio-Psychological Factors in Debre Birhan, Ethiopia , Innovation Journal of Social Sciences and Economic Review: Vol. 6 No. 1 (2024)
- Philipp Christian Lohse, Increasing Transparency for Consumers Showing Probabilities of Virtual Items in Loot Boxes in Digital Games , Innovation Journal of Social Sciences and Economic Review: Vol. 2 No. 3 (2020)
You may also start an advanced similarity search for this article.