Date
Publisher
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.
Study at a glance
- Question: Can anonymous surveys and interviews effectively measure how many students use AI to cheat on programming assignments?
- Setting and data: A large computer science class of 120 students
- Method: Anonymous surveys and interviews (pilot study)
Why it matters
Code-generating tools pose an existential threat to computer science education because students can solve assignments without much effort. While scholars recognize this risk, the proportion of students committing this new AI plagiarism remains an open problem.
Key findings
- In a large computer science class of 120 students, more than 25% of survey respondents admitted to committing AI plagiarism.
- Only one student agreed to be interviewed about their use of AI for cheating.
- Anonymous surveys were effective for capturing self-reported AI misconduct, whereas interviews failed to entice student participation.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
