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What drives students toward ChatGPT? An investigation of the factors influencing adoption and usage of ChatGPT
Abstract
Purpose The purpose of this paper is to identify the factors determining students’ attitude toward using newly emerged artificial intelligence (AI) tool, Chat Generative Pre-Trained Transformer (ChatGPT), for educational and learning purpose based on technology acceptance model. Design/methodology/approach The recommended model was empirically tested with partial least squares structural equation modeling using 375 student survey responses. Findings The study revealed that students have a favorable view of the instructional use of ChatGPT. Usefulness, social presence and legitimacy of the tool, as well as enjoyment and motivation, contribute to a favorable attitude toward using this tool in a learning environment. However, perceived ease of use was not found to be a significant determinant in the adoption and utilization of ChatGPT by the students. Practical implications This research is intended to benefit enterprises, academic institutions and the global community by offering light on how students perceive the ChatGPT service in an educational setting. Furthermore, the application enhances confidence and interest among learners, leading to improved literacy and general awareness. Eventually, the outcome of this research will help AI developers to improve their product and service delivery, as well as benefit regulators in regulating the usage of AI-based bots. Originality/value Due to its novelty, the current research on AI-based ChatGPT usage in the education sector is rather restricted. This study provides the adoption aspects of ChatGPT, a new AI-based technology for students, thereby contributing significantly to the existing research on the adoption of advanced education technologies. In addition, the literature lacks research on the adoption of ChatGPT by students for educational purposes; this study addresses this gap by identifying adoption determinants of ChatGPT in education.
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“The adoption of new technologies is shaped by user perceptions and usage patterns (Venkatesh & Bala, 2008) as well as student characteristics and experiences (Abdaljaleel et al., 2023; Tiwari et al., 2024).”
From “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI (2024) · cites this paper as Tiwari et al., 2024
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Metadata
- Authors
- Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I.
- Affiliations
- University of Technology and Applied Sciences, Muscat (OM)
- German University of Technology (OM)
- Venue
- Interactive Technology and Smart Education
- Publisher
- Emerald
- Published
- 2023-08-29
- Volume
- 21
- Issue
- 3
- Pages
- 333-355
- ISSN
- 1741-5659 (ISSN-L)
- OpenAlex
- W4386178880
- Cited by
- 315
- References
- 109
- Citations by year
- 2026: 75 · 2025: 135 · 2024: 96 · 2023: 9
- FWCI
- 73.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Interactive Technology and Smart Education (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- ChatGPT, generative AI in education, student attitudes, technology acceptance, technology adoption, perceived usefulness, social presence, perceived legitimacy, enjoyment, motivation, perceived ease of use
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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