ChatGPT for Educational Purposes: Investigating the Impact of Knowledge Management Factors on Student Satisfaction and Continuous Usage
Abstract
The growing prevalence of advanced generative AI chatbots, such as ChatGPT, in the educational sector has raised considerable interest in understanding their impact on student knowledge and exploring effective and sustainable implementation strategies. This research investigates the influence of knowledge management factors on the continuous usage of ChatGPT for educational purposes while concurrently evaluating student satisfaction with its use in learning. Using a quantitative approach, a structured questionnaire was administered to 513 Vietnamese university students via Google Forms for data collection. Partial least squares structural equation modeling (PLS-SEM) statistical technique was employed to examine the relationships between identified factors and evaluate the research model. The results provided strong support for several hypotheses, revealing significant positive effects of expectation confirmation on perceived usefulness and satisfaction, as well as perceived usefulness on user satisfaction and continuous usage of ChatGPT. These findings suggest that when students recognize the usefulness of ChatGPT for their learnings, they experience higher satisfaction and are more likely to continue using it. Additionally, knowledge acquisition significantly impacts both satisfaction and continuous usage of ChatGPT, while knowledge sharing and application influence satisfaction exclusively. This indicates that students prioritize knowledge acquisition over sharing and applying knowledge through ChatGPT. The study has theoretical and practical implications for ChatGPT developers, educators, and future research. Theoretically, it contributes to understanding satisfaction and continuous usage in educational settings, utilizing the Expectation-Confirmation Model and integrating knowledge management factors. Practically, it provides insights comprehension and suggestions for enhancing user satisfaction and continuous usage of ChatGPT in education.
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Students who found GenAI tools useful and were satisfied with the tools were more likely to continue using them in the future and recommend them to others.
“Students who found GenAI tools useful and were satisfied with the tools were more likely to continue using them in the future and recommend them to others.”
From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Ngo, Tran, et al., 2024
Most research on GenAI use in educational settings has revealed that students who found it to be useful or helpful were more satisfied with the tool.
“Most research on GenAI use in educational settings has revealed that students who found it to be useful or helpful were more satisfied with the tool”
From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Ngo, Tran, et al., 2024
GenAI in educational settings research has revealed that both perceived usefulness and satisfaction influence students' continued use of GenAI tools for academic and research tasks and recommendations for use by others.
“GenAI in educational settings research has revealed that both perceived usefulness and satisfaction influence students' continued use of GenAI tools for academic and research tasks and recommendations for use by others”
From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Ngo, Tran, et al., 2024
Research on GenAI use in higher education confirms such a relationship, with students whose expectations were met or exceeded reporting greater perceived usefulness and higher overall satisfaction with GenAI tools.
“Research on GenAI use in higher education confirms such a relationship, with students whose expectations were met or exceeded reporting greater perceived usefulness and higher overall satisfaction with GenAI tools”
From Exploring the Role of Generative AI as a Research Assistant in Undergraduate Sociology Research Methods (2026) · cites this paper as Ngo, Tran, et al., 2024
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- Authors
- Ngo, T. T. A., Tran, T. T., An, G. K., & Nguyen, P. T.
- Affiliations
- FPT University (VN)
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2024
- Volume
- 17
- Pages
- 1341-1352
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W4393379744
- Cited by
- 56
- References
- 95
- Citations by year
- 2026: 17 · 2025: 33 · 2024: 6
- FWCI
- 21.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- ChatGPT, generative AI in education, knowledge management, student satisfaction, continuous usage intention, expectation-confirmation model, perceived usefulness, knowledge acquisition, knowledge sharing, knowledge application, university students
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.
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