Plagiarism in the Age of Generative AI: Cheating Method Change and Learning Loss in an Intro to CS Course

Background: ChatGPT became widespread in early 2023 and enabled the broader public to use powerful generative AI, creating a new means for students to complete course assessments. Purpose: In this paper, we explored the degree to which generative AI impacted the frequency and nature of cheating in a large introductory programming course. We also estimate the learning impact of students choosing to submit plagiarized work rather than their own work. Methods: We identified a collection of markers that we believe are indicative of plagiarism in this course. We compare the estimated prevalence of cheating in the semesters before and during which ChatGPT became widely available. We use linear regression to estimate the impact of students' patterns of cheating on their final exam performance. Findings: The patterns associated with these plagiarism markers suggest that the quantity of plagiarism increased with the advent of generative AI, and we see evidence of a shift from online plagiarism hubs (e.g., Chegg, CourseHero) to ChatGPT. In addition, we observe statistically significant learning losses proportional to the amount of presumed plagiarism, but there is no statistical difference on the proportionality between semesters. Implications: Our findings suggest that unproctored exams become increasingly insecure and care needs to be taken to ensure the validity of summative assessments. More importantly, our results suggest that generative AI can be detrimental to students' learning. It seems necessary for educators to reduce the benefit of students using generative AI for counterproductive purposes.