AI on Academic Integrity and Plagiarism Detection

Artificial Intelligence (AI) has introduced transformative advancements in academic integrity and plagiarism detection within educational institutions. This study explores how AI-driven tools are reshaping the landscape of academic honesty by enabling efficient and precise detection of plagiarised content, particularly in the face of sophisticated paraphrasing and AI-generated text. We analyse the effectiveness of traditional and AI-powered plagiarism detection tools, comparing their capabilities in recognising rephrased, translated, and synthetically generated content. Through a mixed-method approach, including quantitative tool performance analysis and qualitative insights from educators and students, the study assesses both the benefits and ethical challenges posed by AI in academic settings. Findings reveal that while AI significantly enhances detection accuracy, it raises concerns regarding dependency on automated assessments and ethical considerations in student evaluation. This research underscores the need for complementary human oversight and advocates for policy frameworks that guide the responsible integration of AI in academia.

Authors

Publication Details

Journal
ASM Science Journal
Published
2025-03-28
DOI
https://doi.org/10.32802/asmscj.2025.1918
Citations
21
Primary Topic
Academic integrity and plagiarism
Type
article
Field-Weighted Citation Impact
49.68
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article

AI on Academic Integrity and Plagiarism Detection

Zhang Jing Bing, Wai Yie Leong
21 citations
ASM Science Journal
Academic integrity and plagiarism
49.68
article

AI on Academic Integrity and Plagiarism Detection

Zhang Jing Bing, Wai Yie Leong
article en
21 citations

Abstract

Artificial Intelligence (AI) has introduced transformative advancements in academic integrity and plagiarism detection within educational institutions. This study explores how AI-driven tools are reshaping the landscape of academic honesty by enabling efficient and precise detection of plagiarised content, particularly in the face of sophisticated paraphrasing and AI-generated text. We analyse the effectiveness of traditional and AI-powered plagiarism detection tools, comparing their capabilities in recognising rephrased, translated, and synthetically generated content. Through a mixed-method approach, including quantitative tool performance analysis and qualitative insights from educators and students, the study assesses both the benefits and ethical challenges posed by AI in academic settings. Findings reveal that while AI significantly enhances detection accuracy, it raises concerns regarding dependency on automated assessments and ethical considerations in student evaluation. This research underscores the need for complementary human oversight and advocates for policy frameworks that guide the responsible integration of AI in academia.

ASM Science JournalVol. 20(1)
Openalex Percentile: Top 0%
Academic integrity and plagiarism
49.68
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AI on Academic Integrity and Plagiarism Detection — Zhang Jing Bing, Wai Yie Leong · ASM Science Journal (2025) | TGRS Research Map | TGRS