Online Archive of University of Virginia Scholarship
LLMs and Cheating in Academic Settings: A Meta Review of the Literature Surrounding Contract Cheating in Computer Science; Responses to AI-Assisted Cheating in Higher Education80 views
Author
Falcon-Flansburgh, Victoria, School of Engineering and Applied Science, University of Virginia0000-0002-6032-9700
Advisors
Norton, Peter, EN-Engineering and Society, University of Virginia
Morrison, Briana, EN-Comp Science Dept, University of Virginia
Abstract
As Large Language Models (LLMs) have intensified debates over cheating in higher education, how have software engineers and academic communities responded?
AI has simplified cheating in higher education, particularly in computer science because methods of detection that use linguistic fingerprint do not apply to code. New and developing methods to combat cheating in computer science are reviewed and assessed.
LLMs have opened new avenues of cheating. To justify cheating, students typically cite ambiguous course policies or perceived unfairness. As LLMs impair the efficacy of technical means of cheating detection, social techniques, such as methods that promote the inherent rewards of learning or the personal satisfactions of work well done, may offer more promising alternatives.
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Briana Morrison
STS Advisor: Peter Norton
Technical Team Members: Victoria Falcon-Flansburgh
Falcon-Flansburgh, Victoria. LLMs and Cheating in Academic Settings: A Meta Review of the Literature Surrounding Contract Cheating in Computer Science; Responses to AI-Assisted Cheating in Higher Education. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2025-12-12, https://doi.org/10.18130/arws-6977.
