Exploring The Ethical Use Of LLM Chatbots In Higher Education
Open accessAbstract
The rise of LLM chatbots has created complex gray areas regarding plagiarism, blurring the once-clear boundaries between original creation and derivation. This has raised significant concerns about academic integrity in higher education, with reports indicating that students are misusing these tools for academic assignments. However, research on the factors that can promote ethical use remains limited. Grounded in social cognitive and rational choice theories, we propose a model that integrates environmental factors (ethical climate and ethical supervision), personal factors (moral consciousness and ethical chatbot self-efficacy), a technological factor (awareness of chatbot usage policies), and risk factors (AI writing detection probability and sanction severity). We collected data from 703 US students and analyzed the sample using PLS-SEM and fsQCA. The findings reveal that all hypothesized relationships, except for awareness of chatbot usage policies, are supported. Additionally, fsQCA identifies three configurations associated with high and low ethical use.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Students are less likely to engage in questionable academic practices when they perceive a higher probability of being caught or punished.
“While when they perceive a higher probability of being caught or punished, they are less likely to engage in questionable academic practices (Cochran et al., 1999;Freiburger et al., 2017;Mvondo et al., 2023;Williams & Hosek, 2003).”
From Chat or cheat? Academic dishonesty, risk perceptions, and ChatGPT usage in higher education students (2025) · cites this paper as Mvondo et al., 2023
According to the RCT, individuals weigh potential risks of using ChatGPT against benefits to make a rational decision about whether to use ChatGPT.
“According to the RCT, individuals weigh the potential risks of using ChatGPT (such as the fear of violating university policies) against the benefits (such as improved assignment outcomes) to make a rational decision about whether to use ChatGPT”
From Chat or cheat? Academic dishonesty, risk perceptions, and ChatGPT usage in higher education students (2025) · cites this paper as Mvondo et al., 2023
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 129 · Cited by 33
- Authors
- Mvondo, G. F. N., Niu, B., & Eivazinezhad, S.
- Affiliations
- Shenzhen University (CN)
- Shahid Beheshti University (IR)
- Corresponding author
- Niu, Ben
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 200
- Pages
- 115575-115575
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W4412423726
- Cited by
- 33
- References
- 129
- Citations by year
- 2026: 10 · 2025: 14 · 2024: 8 · 2023: 1
- FWCI
- 24.67
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Journal of Business Research (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Funders
- National Natural Science Foundation of China (71971143)
- Natural Science Foundation of Guangdong Province (2019A1515110401, 2020A1515010752)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Social Sciences › Decision Sciences › Information Systems and Management › Ethics in Business and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- large language model chatbots, ethical use of AI, academic integrity, plagiarism, university students, ethical climate, ethical supervision, moral consciousness, severity of punishment, AI-assisted writing
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 ›
Are you an author, or do you see a problem with this page? Request a review
