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

TL;DR

This pilot study investigates the prevalence of AI-assisted cheating in a large CS class, finding that over 25% of students admit to AI plagiarism, highlighting the need for effective detection methods.

Contribution

The study demonstrates the feasibility of using anonymous surveys to estimate AI plagiarism prevalence in programming courses.

Findings

Over 25% of students admitted to AI plagiarism

Surveys are effective for assessing AI cheating prevalence

Interviews had very low participation

Abstract

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.