Plagiarism and AI Assistance Misuse in Web Programming: Unfair Benefits and Characteristics

Oscar Karnalim, Hapnes Toba, Meliana Christianti Johan, Erico Darmawan Handoyo, Yehezkiel David Setiawan +1 more
10/31/2023
cs.AIcs.CY

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Abstract

In programming education, plagiarism and misuse of artificial intelligence (AI) assistance are emerging issues. However, not many relevant studies are focused on web programming. We plan to develop automated tools to help instructors identify both misconducts. To fully understand the issues, we conducted a controlled experiment to observe the unfair benefits and the characteristics. We compared student performance in completing web programming tasks independently, with a submission to plagiarize, and with the help of AI assistance (ChatGPT). Our study shows that students who are involved in such misconducts get comparable test marks with less completion time. Plagiarized submissions are similar to the independent ones except in trivial aspects such as color and identifier names. AI-assisted submissions are more complex, making them less readable. Students believe AI assistance could be useful given proper acknowledgment of the use, although they are not convinced with readability and correctness of the solutions.

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Cite this paper

@article{karnalim2023plagiarism,
  title  = {Plagiarism and AI Assistance Misuse in Web Programming: Unfair Benefits and Characteristics},
  author = {Oscar Karnalim and Hapnes Toba and Meliana Christianti Johan and Erico Darmawan Handoyo and Yehezkiel David Setiawan and Josephine Alvina Luwia},
  year   = {2023},
  eprint = {2310.20104},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.20104v1}
}

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