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Assessing the Prevalence of AI-assisted Cheating in Programming Courses: A Pilot Study

arXiv 2507.06438 ↗First posted 8 Jul 2025cs.CY cs.AI cs.HCcited by 0

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Abstract (arXiv)

Tools that can generate computer code in response to inputs written in natural language, such as ChatGPT, pose an existential threat to Computer Science education in its current form, since students can now use these tools to solve assignments without much effort. While that risk has already been recognized by scholars, the proportion of the student body that is incurring in this new kind of plagiarism is still an open problem. We conducted a pilot study in a large CS class (n=120) to assess the feasibility of estimating AI plagiarism through anonymous surveys and interviews. More than 25% of the survey respondents admitted to committing AI plagiarism. Conversely, only one student accepted to be interviewed. Given the high levels of misconduct acknowledgment, we conclude that surveys are an effective method for studies on the matter, while interviews should be avoided or designed in a way that can entice participation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 9 references that are in the map. First posted on arXiv 8 Jul 2025.

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References · 2

  1. 2022Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Languagecited by 82
  2. 2023ChatGPT, Can You Generate Solutions for my Coding Exercises? An Evaluation on its Effectiveness in an undergraduate Java Programming Coursecited by 12

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