Investigating Undergraduate Students’ Intention to Engage in Artificial Intelligence Learning
Main Article Content
This study examines how contextual factors and influences shape undergraduate students' attitudes and behaviors regarding artificial intelligence (AI) learning. Globally, AI is becoming increasingly important in education, making it imperative to understand the readiness and intentions of students for AI-enhanced learning. One hundred and ninety-two higher education students in Nigeria were surveyed online about their confidence, attitudes, self-efficacy, and age–the relationships between these factors and students' intentions to learn through AI were analysed using structural equation modelling. A significant direct association was found between confidence, attitudes, self-efficacy, and subjective norms and the intention of students to use AI. Mediation analyses demonstrated that attitudes significantly mediate the relationship between subjective norms and intention. Perceived usefulness is therefore of paramount importance. Despite this, age does not play a significant role in modifying these relationships. A review of the study’s quality criteria, including fit indexes and reliability measures, indicated that the model reasonably fit the data. The study provides valuable insights into the factors influencing undergraduate students' intentions to engage in AI learning
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D. and Oyelere, S. S. "Teachers’ readiness and intention to teach artificial intelligence in schools." Computers and Education: Artificial Intelligence 3, (2022): 100099. https:/f/doi.org/10.1016/j.caeai.2022.100099
Bedué, P. and Fritzsche, A. "Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption." Journal of Enterprise Information Management 35, no. 2 (2022): 530–549. https://doi.org/10.1108/JEIM-06-2020-0233
Byrne, B. M. Structural equation modeling with EQS: Basic concepts, applications, and programming. 2nd ed. Routledge, 2008.
Chai, C. S., Chiu, T. K. F., Wang, X., Jiang, F. and Lin, X.-F. "Modeling Chinese secondary school students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory." Sustainability 15, no. 1 (2022): 605. https://doi.org/10.3390/su15010605
Chai, C. S., Lin, P.-Y., Jong, M. S., Dai, Y., Chiu, T. K. F. and Huang, B. "Factors influencing students’ behavioral intention to continue artificial intelligence learning." 2020 International Symposium on Educational Technology (ISET) (2020a): 147–150. https://doi.org/10.1109/ISET49818.2020.00040
Chai, C. S., Wang, X. and Xu, C. "An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence." Mathematics 8, no. 11 (2020b): 2089. https://doi.org/10.3390/math8112089
Chang, Y., Lee, S., Wong, S. F. and Jeong, S. "AI-powered learning application use and gratification: An integrative model." Information Technology & People 35, no. 7 (2020b): 2115–2139. https://doi.org/10.1108/ITP-09-2020-0632
Chen, W., Liu, C., Xing, F., Peng, G. and Yang, X. "Establishment of a maturity model to assess the development of industrial AI in smart manufacturing." Journal of Enterprise Information Management 35, no. 3 (2022): 701–728. https://doi.org/10.1108/JEIM-10-2020-0397
Cheng, E. W. L. "Choosing between the theory of planned behavior (TPB) and the technology acceptance model (TAM)." Educational Technology Research and Development 67, no. 1 (2019): 21–37. https://doi.org/10.1007/s11423-018-9598-6
Choudhury, A. "Toward an ecologically valid conceptual framework for the use of artificial intelligence in clinical settings: Need for systems thinking, accountability, decision-making, trust, and patient safety considerations in safeguarding the technology and clinicians." JMIR Human Factors 9, no. 2 (2022): e35421. https://doi.org/10.2196/35421
Crompton, H. and Song, D. "The potential of artificial intelligence in higher education." Revista Virtual Universidad Católica Del Norte 62, (2021): 1–4. https://doi.org/10.35575/rvucn.n62a1
Çakmak, F. "Chatbot-human interaction and its effects on EFL students’ L2 speaking performance and speaking anxiety." Novitas-ROYAL (Research on Youth and Language), 16, no. 2 (2022): 113–131.
Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. "User acceptance of computer technology: A comparison of two theoretical models." Management Science 35, no. 8 (1989): 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Elshaer, I. A. and Sobaih, A. E. E. "Antecedents of risky financial investment intention among higher education students: A mediating moderating model using structural equation modeling." Mathematics 11, no. 42 (2023): 353. https://doi.org/10.3390/math11020353
Fornell, C. and Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18, no. 1 (1981): 39-50.
Fu, S., Gu, H. and Yang, B. "The affordances of AI‐enabled automatic scoring applications on learners’ continuous learning intention: An empirical study in China." British Journal of Educational Technology 51, no. 5 (2020): 1674–1692. https://doi.org/10.1111/bjet.12995
Gado, S., Kempen, R., Lingelbach, K. and Bipp, T. "Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence?" Psychology Learning & Teaching 21, no. 1 (2022): 37–56. https://doi.org/10.1177/14757257211037149
Gangwar, H., Date, H. and Ramaswamy, R. "Developing a cloud-computing adoption framework." Global Business Review 16, no. 4 (2015): 632–651. https://doi.org/10.1177/0972150915581108
Hair, J. F., Ringle, C. M. and Sarstedt, M. PLS-SEM: "Indeed a silver bullet." Journal of Marketing Theory and Practice 19, no. 2 (2011): 139–152. https://doi.org/10.2753/MTP1069-6679190202
Henseler, J., Ringle, C. M. and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43 (2015): 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hinojo-Lucena, F. J., Aznar-Díaz, I., Cáceres-Reche, M. P. and Romero-Rodríguez, J. M. "Artificial intelligence in higher education: A bibliometric study on its impact in the scientific literature." Education Sciences 9, no. 1 (2019): 51.
Hong, X., Zhang, M. and Liu, Q. "Preschool teachers’ technology acceptance during the COVID-19: An adapted technology acceptance model." Frontiers in Psychology 12, (2021): https://doi.org/10.3389/fpsyg.2021.691492
Hoong, A. L. S., Thi, L. S. and Lin, M.-H. "Affective technology acceptance model: Extending technology acceptance model with positive and negative affect." Knowledge management strategies and applications. (2017). https://doi.org/10.5772/intechopen.70351
Hsiao, C.-H. and Tang, K.-Y. "Explaining undergraduates’ behavior intention of e-textbook adoption." Library Hi Tech 32, no. 1 (2014): 139–163. https://doi.org/10.1108/LHT-09-2013-0126
Hua, W. "Exploring the role of artificial intelligence in financial decision-making: Challenges and opportunities." Journal of Financial Technology and AI 10, no. 1 (2022): 55–72. https://doi.org/10.1016/j.jftai.2022.01.003
Islam, A. K. M. N., Azad, N., Mäntymäki, M. and Islam, S. M. S. TAM and e-learning adoption: A philosophical scrutiny of TAM, its limitations, and prescriptions for e-learning adoption research. In H. Li, M. Mäntymäki & X. Zhang (Eds.), Digital services and information intelligence. IFIP advances in information and communication technology 445, (2014): 164–175). Springer. https://doi.org/10.1007/978-3-662-45526-5_16
Jaafar, M., Rasoolimanesh, S. M. and Ismail, S. "Perceived sociocultural impacts of tourism and community participation: A case study of Langkawi Island." Tourism and Hospitality Research 17, no. 2 (2017): 123–134. https://doi.org/10.1177/1467358415610373
Kwak, Y., Ahn, J.-W. and Seo, Y. H. "Influence of AI ethics awareness, attitude, anxiety, and self-efficacy on nursing students’ behavioral intentions." BMC Nursing 21, no. 1 (2022): 267. https://doi.org/10.1186/s12912-022-01048-0
Lee, J. and Cho, H. "The impact of artificial intelligence on the future of accounting." Journal of Emerging Technologies in Accounting 18, no. 2 (2021): 89–108. https://doi.org/10.2308/jeta-2021-0045
Leimanis, A. and Palkova, K. "Ethical guidelines for artificial intelligence in healthcare from the sustainable development perspective." European Journal of Sustainable Development 10, no. 1 (2021): 90. https://doi.org/10.14207/ejsd.2021.v10n1p90
Li, X., Jiang, M. Y., Jong, M. S., Zhang, X. and Chai, C. Understanding medical students’ perceptions of and behavioral intentions toward learning artificial intelligence: A survey study. International Journal of Environmental Research and Public Health 19, no. 14 (2022): 8733. https://doi.org/10.3390/ijerph19148733
Lin, X. and Powell, S. R. "The roles of initial mathematics, reading, and cognitive skills in subsequent mathematics performance: A meta-analytic structural equation modeling approach." Review of Educational Research 92, no. 2 (2022): 288–325. https://doi.org/10.3102/00346543211054576
Lu, K., Pang, F. and Shadiev, R. "Understanding college students’ continuous usage intention of asynchronous online courses through extended technology acceptance model." Education and Information Technologies 28, no. 8 (2023): 9747–9765. https://doi.org/10.1007/s10639-023-11591-1
Lukianets, H. and Lukianets, T. "Promises and perils of AI use on the tertiary educational level." Grail of Science 25, (2023): 306–311. https://doi.org/10.36074/grail-of-science.17.03.2023.053
Ma, W. W., Andersson, R. and Streith, K. "Examining user acceptance of computer technology: An empirical study of student teachers." Journal of Computer Assisted Learning 21, no. 6 (2005): 387–395. https://doi.org/10.1111/j.1365-2729.2005.00145.x
McCord, M. Technology acceptance model. In Handbook of research on electronic surveys and measurements (2007): 306–308. IGI Global. https://doi.org/10.4018/978-1-59140-792-8.ch038
Mohamed, N., Oubelaid, A. and Almazrouei, S. khameis. "Staying ahead of threats: A review of AI and cyber security in power generation and distribution." International Journal of Electrical and Electronics Research 11, no. 1 (2023): 143–147. https://doi.org/10.37391/ijeer.110120
Molenaar, I. “The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning.” Computers and Education: Artificial Intelligence 3, (2022): 100070. https://doi.org/10.1016/j.caeai.2022.100070
Mousavi, B., Seyyedeh, F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S. and Kimiafar, K. "Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: A systematic review." Health Science Reports 6, no. 3 (2023). https://doi.org/10.1002/hsr2.1138
Park, W. and Kwon, H. "Implementing artificial intelligence education for middle school technology education in Republic of Korea." Internation Journal of Technol Design Education 34, (2024): 109–135. https://doi.org/10.1007/s10798-023-09812-2
Peng, M., Xie, J., Xiong, M. and Liu, Y. "Artificial Intelligence education in primary and secondary schools from the perspective of thinking quality." Journal of Contemporary Educational Research 7, no. 4 (2023): 41–46. https://doi.org/10.26689/jcer.v7i4.4875
Punshon, T., Li, Z., Marsit, C. J., Jackson, B. P., Baker, E. R. and Karagas, M. R. "Placental metal concentrations in relation to maternal and infant toenails in a U.S. cohort." Environmental Science & Technology 50, no. 3 (2016): 1587–1594. https://doi.org/10.1021/acs.est.5b05316
Sarker, I. H. "AI-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems." SN Computer Science 3, no. 2 (2022): 158. https://doi.org/10.1007/s42979-022-01043-x
Scherer, R., Siddiq, F. and Tondeur, J. "The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education." Computers & Education 128, (2019): 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Shang, G., Low, S. P. and Lim, X. Y. V. "Prospects, drivers of and barriers to artificial intelligence adoption in project management." Built Environment Project and Asset Management 13, no. 5 (2023): 629–645. https://doi.org/10.1108/BEPAM-12-2022-0195
Şimşek, A. S. and Ateş, H. "The extended technology acceptance model for Web 2.0 technologies in teaching." Innoeduca. International Journal of Technology and Educational Innovation 8, no. 2 (2022): 165–183. https://doi.org/10.24310/innoeduca.2022.v8i2.15413
Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L. and Poon, D. S. "Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: A multicentre survey." Insights into Imaging 11, no. 1 (2020): 14. https://doi.org/10.1186/s13244-019-0830-7
Song, J., Zhang, L., Yu, J., Peng, Y., Ma, A. and Lu, Y. "Paving the way for novices: How to teach AI for K-12 education in China." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 12852–12857. https://doi.org/10.1609/aaai.v36i11.21565
Teo, T., Lee, C. B., Chai, C. S. and Wong, S. L. "Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the technology acceptance model (TAM)." Computers & Education 53, no. 3 (2009): 1000–1009. https://doi.org/10.1016/j.compedu.2009.05.017
Tran, A. Q., Nguyen, L. H., Nguyen, H. S. A., Nguyen, C. T., Vu, L. G., Zhang, M., Vu, T. M. T., Nguyen, S. H., Tran, B. X., Latkin, C. A., Ho, R. C. M. and Ho, C. S. H. "Determinants of intention to use artificial intelligence-based diagnosis support system among prospective physicians." Frontiers in Public Health 9, (2021). https://doi.org/10.3389/fpubh.2021.755644
Venkatesh, V. and Bala, H. "Technology acceptance model 3 and a research agenda on interventions." Decision Sciences 39, no. 2 (2008): 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh, V. and Davis, F. D. "A theoretical extension of the technology acceptance model: Four longitudinal field studies." Management Science 46, no. 2 (2000): 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
Voorhees, C. M., Brady, M. K., Calantone, R. and Ramirez, E. "Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies." Journal of the Academy of Marketing Science 44, no. 1 (2016): 119–134. https://doi.org/10.1007/s11747-015-0455-4
Wagner, G., Raymond, L. and Paré, G. "Understanding prospective physicians’ intention to use artificial intelligence in their future medical practice: Configurational analysis." JMIR Medical Education 9, (2023): e45631. https://doi.org/10.2196/45631
Wang, F. and Wang, X. "Tracing theory diffusion: A text mining and citation-based analysis of TAM." Journal of Documentation 76, no. 6 (2020): 1109–1134. https://doi.org/10.1108/JD-02-2020-0023
Wang, X., Wang, P., Wang, P., Cao, M. and Xu, X. "Relationships among mental health, social capital and life satisfaction in rural senior older adults: A structural equation model." BMC Geriatrics, 22, no. 1 (2022): 73. https://doi.org/10.1186/s12877-022-02761-w
Wu, C., Li, Y., Li, J., Zhang, Q. and Wu, F. "Web-based platform for K-12 AI education in China." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 15687–15694. https://doi.org/10.1609/aaai.v35i17.17848
Xu, W. and Ouyang, F. "The application of AI technologies in STEM education: A systematic review from 2011 to 2021." International Journal of STEM Education 9, no. 59 (2022). https://doi.org/10.1186/s40594-022-00377-5
Yang, M., Moon, J., Yang, S., Oh, H., Lee, S., Kim, Y. and Jeong, J. "Design and implementation of an explainable bidirectional LSTM model based on transition system approach for cooperative AI-workers." Applied Sciences 12, no. 13 (2022): 6390. https://doi.org/10.3390/app12136390
Yangın Ersanlı, C. "The effect of using augmented reality with storytelling on young learners’ vocabulary learning and retention." Novitas-ROYAL (Research on Youth and Language) 17, no. 1 (2023): 62–72.
Yau, K. W., Chai, C. S., Chiu, T. K. F., Meng, H., King, I. and Yam, Y. "A phenomenographic approach on teacher conceptions of teaching artificial intelligence (AI) in K-12 schools." Education and Information Technologies 28, no. 1 (2023): 1041–1064. https://doi.org/10.1007/s10639-022-11161-x
Yildirim, H., Barut, M. and Gungor, O. "Artificial intelligence in accounting: Evaluation and practices." Journal of Artificial Intelligence in Accounting 8, no. 3 (2021): 123–145. https://doi.org/10.1016/j.artintacct.2021.05.001
Zawacki-Richter, O., Marín, V. I., Bond, M. and Gouverneur, F. "Systematic review of research on artificial intelligence applications in higher education – where are the educators?" International Journal of Educational Technology in Higher Education 16, no. 1 (2019): 39. https://doi.org/10.1186/s41239-019-0171-0
Zhang, L. "AI-driven innovations in accounting: A review of recent developments." International Journal of Accounting and AI 15, no. 4 (2023): 201–219. https://doi.org/10.1080/ijaa.2023.01452
Zolait, A., Radhi, N., Alhowaishi, M. M., Sundram, V. P. K. and Aldoseri, L. M. "Can Bahraini patients accept e-health systems?" International Journal of Health Care Quality Assurance 32, no. 4 (2019): 720–730. https://doi.org/10.1108/IJHCQA-05-2018-0106