Artificial intelligence (AI) leverages mathematical algorithms to emulate human cognitive abilities, leading to a transformative impact on the
education sector. Educators are at the front lines of implementing AI in the classroom. Recent scientific studies demonstrate the capacity of AI,
particularly generative models like ChatGPT, to reshape various aspects of education. In a recent study, we showcased that the integration of
both artificial intelligence, specifically ChatGPT, and interactive learning activities significantly enhances the engagement levels of STEM
students enrolled in a General Biology course. Furthermore, this combined approach not only boosts student engagement but also demonstrates
an improvement in their overall performance within the course. Building on preliminary studies, the objective of this review article is to delineate
the diverse applications of generative AI in education. To achieve this objective, we conducted a thorough search across scientific databases,
including Google Scholar, Science Direct, government websites, and other resources, to collect relevant papers. Our findings underscore the
contributions of generative AI, exemplified by ChatGPT, in enabling students to generate innovative text for written assignments, providing
personalized feedback, facilitating adaptive learning, enhancing accessibility to education by eliminating barriers for individuals with disabilities,
and supporting research endeavors.
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This content will become publicly available on July 18, 2025
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.
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- Award ID(s):
- 2144249
- NSF-PAR ID:
- 10519255
- Publisher / Repository:
- ACM
- Date Published:
- Subject(s) / Keyword(s):
- plagiarism detection cheating generative AI LLM CS 1
- Format(s):
- Medium: X
- Location:
- Atlanta, GA
- Sponsoring Org:
- National Science Foundation
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