Correctness, Convergence, and AI-Generated Code Detection: A Longitudinal Study of Student and Large Language Model Code in Introductory Programming

A study investigated the challenges of detecting AI-generated code in introductory programming, particularly when Large Language Models (LLMs) can produce plausible solutions that may closely resemble legitimate student work due to the limited 'natural implementations' of certain assignments. The research utilized a large dataset of student submissions and LLM-generated solutions to analyze the efficacy of generated-reference matching techniques.

The increasing sophistication of AI in generating code presents significant challenges for assessing originality and integrity in technical work and educational settings. Understanding how to accurately differentiate between human and AI-generated content is crucial for maintaining fair evaluation systems and developing effective strategies to leverage or mitigate AI's impact on code production.

Large Language Models (LLMs) are capable of generating plausible solutions to programming assignments.

Forward consideration, not a verified fact.