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International Journal of Electrical and Computer Engineering (IJECE)
Article . 2026 . Peer-reviewed
License: CC BY SA
Data sources: Crossref
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The ethics of AI technology in academic work: assessing the line between assistance and plagiarism

Authors: Md. Owafeeuzzaman Patwary; Md. Reazul Islam; Abtahi Islam; Nur-e Sarjina Khan; Md. Abdullah Al–Jubair; Md. Jakir Hossen; M. F. Mridha;

The ethics of AI technology in academic work: assessing the line between assistance and plagiarism

Abstract

The integration of artificial intelligence (AI) into academia has transformed educational practices and enhanced personalized learning and problem-solving capabilities. However, this raises significant ethical concerns regarding the balance between legitimate assistance and plagiarism. This study investigated public perceptions of AI in academic settings, focusing on its impact on effectiveness, dependency, and ethical considerations of AI use. A survey of 498 respondents from various educational roles was conducted, and the data were analyzed using SPSS for descriptive statistics, chi-square tests, and regression analyses. The results identified a significant correlation between people’s educational roles and their interaction with AI tools (χ2(6) = 16.488, p = 0.036), reflecting the diverse patterns of interaction within the academic community. More frequent use of AI was linked to less dependency (β = −0.298, p < 0.001), contradicting the widespread belief of over-reliance on AI. Age and educational role had limited explanatory value in perception of AI dependency issues (R2 = 0.033). The findings indicate a strong correlation between AI usage frequency and dependency levels, with increased exposure to AI fostering a more critical approach rather than a dependent one. Concerns regarding the unethical use of AI, inaccuracies in AI-generated content, and the need for clear institutional policies were also highlighted. This study underscores the importance of responsible AI integration, advocating for ethical frameworks and educational interventions to ensure that AI enhances learning without compromising academic integrity.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average