Navigating the digital learning landscape: insights into ethical dilemmas and academic misconduct among university students
Abstract The impact of COVID-19 has significantly expanded the use of the internet in education, and artificial intelligence technologies such as ChatGPT have become increasingly prominent in the educational field; however, these advancements entail challenges pertaining to academic integrity in hig...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-05-01
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| Series: | International Journal of Educational Technology in Higher Education |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41239-025-00516-2 |
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| Summary: | Abstract The impact of COVID-19 has significantly expanded the use of the internet in education, and artificial intelligence technologies such as ChatGPT have become increasingly prominent in the educational field; however, these advancements entail challenges pertaining to academic integrity in higher education. To understand the prevalence of online academic dishonesty (E-AD), this study examines the relationships among personal characteristics, the Ethical Dissonance Index (EDI), perceived severity of harm, online academic ethical judgment, and E-AD among 522 Chinese university students. The findings reveal that science students are more likely to engage in online plagiarism than are humanities students. Male students are more likely to engage in both online plagiarism and cheating than are female students. In addition, female students also exhibit higher perceived severity of harm from both perpetrators’ and nonperpetrators’ perspectives. A cluster analysis of the EDI identified four clusters: pervasive/legitimate, uncommon/illegitimate, pervasive/illegitimate, and uncommon/legitimate. Additionally, the four types of E-AD—plagiarism, facilitation, fabrication, and cheating—exhibited significant negative correlations with perceived harm and online academic ethical judgment among both perpetrators and nonperpetrators. These dishonest behaviors were also positively correlated with each other. Regression analysis further revealed that students' online academic ethical judgments constitute a common predictor of all types of E-AD. This study provides a comprehensive understanding of E-AD among Chinese university students and offers empirical evidence that can inform educational policies and practices. |
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| ISSN: | 2365-9440 |