Artificial intelligence and academic integrity: exploring plagiarism in Ecuadorian universities

Plagiarism in higher education has long been a concern, exacerbated by the emergence of Generative Artificial Intelligence (GenAI), which has heightened worries about academic integrity. This study examined the association between GenAI use and self-reported plagiarism among university students in Ecuador. Data were collected from 4,811 students across eight universities through a validated questionnaire (α = 0.73) that explored academic and non-academic technology use. A binary logistic regression was applied, with plagiarism level as the dependent variable. The model explained 20.2% of the variance (Nagelkerke R² = 0.202) and identified key predictors: reporting the use of ChatGPT to instructors (OR = 1.223, p < 0.001) and trusting ChatGPT-generated information (OR = 1.176, p < 0.001) were positively associated with plagiarism, while perceiving that AI improves academic performance (OR = 0.945, p < 0.01) and possessing broader knowledge of AI tools (OR = 0.911, p < 0.01) were associated with lower plagiarism levels. Although the study’s cross-sectional design limits causal inference, findings highlight the importance of ethical awareness, digital literacy, and institutional policies for responsible GenAI integration. This research contributes to understanding how GenAI use interacts with academic integrity in higher education and informs evidence-based approaches to promote ethical and transparent learning practices.

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Publication Details

Journal
International Journal for Educational Integrity
Published
2025-12-25
DOI
https://doi.org/10.1007/s40979-025-00209-3
Primary Topic
Academic integrity and plagiarism
Type
article
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article

Artificial intelligence and academic integrity: exploring plagiarism in Ecuadorian universities

Josep M. Duart, Juan Carlos Torres-Diaz, Diana Rivera, Ana Beltran Flandoli
International Journal for Educational Integrity
Academic integrity and plagiarism
article

Artificial intelligence and academic integrity: exploring plagiarism in Ecuadorian universities

Josep M. Duart, Juan Carlos Torres-Diaz, Diana Rivera, Ana Beltran Flandoli
article en

Abstract

Plagiarism in higher education has long been a concern, exacerbated by the emergence of Generative Artificial Intelligence (GenAI), which has heightened worries about academic integrity. This study examined the association between GenAI use and self-reported plagiarism among university students in Ecuador. Data were collected from 4,811 students across eight universities through a validated questionnaire (α = 0.73) that explored academic and non-academic technology use. A binary logistic regression was applied, with plagiarism level as the dependent variable. The model explained 20.2% of the variance (Nagelkerke R² = 0.202) and identified key predictors: reporting the use of ChatGPT to instructors (OR = 1.223, p < 0.001) and trusting ChatGPT-generated information (OR = 1.176, p < 0.001) were positively associated with plagiarism, while perceiving that AI improves academic performance (OR = 0.945, p < 0.01) and possessing broader knowledge of AI tools (OR = 0.911, p < 0.01) were associated with lower plagiarism levels. Although the study’s cross-sectional design limits causal inference, findings highlight the importance of ethical awareness, digital literacy, and institutional policies for responsible GenAI integration. This research contributes to understanding how GenAI use interacts with academic integrity in higher education and informs evidence-based approaches to promote ethical and transparent learning practices.

International Journal for Educational IntegrityVol. 21(1)
Universidad Técnica Particular de Loja (EC), Universitat Oberta de Catalunya (ES)
Quality Education
Openalex Percentile: Top 28%
Academic integrity and plagiarism
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Artificial intelligence and academic integrity: exploring plagiarism in Ecuadorian universities — Josep M. Duart, Juan Carlos Torres-Diaz, et al. · International Journal for Educational Integrity (2025) | TGRS Research Map | TGRS