The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT
Open accessPublished version of a preprint
Abstract
Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students’ attitudes and usage of ChatGPT in Arab countries. The survey instrument “TAME-ChatGPT” was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the “TAME-ChatGPT” constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated “TAME-ChatGPT” as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
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Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133
Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126
Self-citationUnderstanding the role of digital technologies in education: A review
Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022) · Sustainable Operations and Computers · doi:10.1016/j.susoc.2022.05.004 · Cited by 2,647
Ray, P. P. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.003 · Cited by 2,463 · cited 3×
“As previously illustrated in various studies, ChatGPT responds to queries in various languages, with notable capabilities facilitating the generation of coherent responses 11,30,80–82 .”
BackgroundMethodTechnology acceptance model: a literature review from 1986 to 2013
Marangunić, N., & Granić, A. (2015) · Universal Access in the Information Society · doi:10.1007/s10209-014-0348-1 · Cited by 2,458
Conceptualizing AI literacy: An exploratory review
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100041 · Cited by 2,205
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024
Artificial intelligence in higher education: the state of the field
Crompton, H., & Burke, D. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00392-8 · Cited by 2,010
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“ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI
Baek, C., Tate, T., & Warschauer, M. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100294 · Cited by 306
ChatGPT in the higher education: A systematic literature review and research challenges
Baig, M. I., & Yadegaridehkordi, E. (2024) · International Journal of Educational Research · doi:10.1016/j.ijer.2024.102411 · Cited by 208
Generative AI in education and research: A systematic mapping review
Yusuf, A., Pervin, N., Román‐González, M., & Noor, N. M. (2024) · Review of Education · doi:10.1002/rev3.3489 · Cited by 182
Enhancing academic integrity among students in GenAI Era:A holistic framework
Rasul, T., Nair, S., Kalendra, D., Balaji, M. S., Santini, F. D. O., Ladeira, W. J., Rather, R. A., Yasin, N., Rodriguez, R. V., Kokkalis, P., et al. (2024) · The International Journal of Management Education · doi:10.1016/j.ijme.2024.101041 · Cited by 82
Metadata
- Authors
- Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., Khatib, S. E., Rahal, M., Sahban, A., Abdelaziz, D. H., Mansour, N. O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., Hallit, S., & Sallam, M.
- Venue
- Scientific Reports
- Published
- 2024-01-23
- Volume
- 14
- Issue
- 1
- Pages
- 1983-1983
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W4391137578
- Cited by
- 291
- References
- 102
- FWCI
- 110.96
- Type
- journal article
- Language
- en
- Open access
- gold
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- Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., Khatib, S. E., Rahal, M., Sahban, A., Abdelaziz, D. H., Mansour, N. O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., . . . Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports. https://doi.org/10.1038/s41598-024-52549-8
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
