Year: 2023
Type: article
Abstract: Large language models (LLMs) such as OpenAI's GPT-4 have transformed natural language processing with their ability to understand context and generate human-like text. This has led to considerable debate, especially in the education sector, where LLMs can enhance learning but also pose challenges to academic integrity. Detecting AI-generated content (AIGC) is difficult, as existing methods struggle to keep pace with advancements in generation technology. This research proposes a novel approach to AIGC detection in short essays, using digital behavior capture and follow-up questioning to verify text authorship. We executed a controlled experiment as an initial evaluation to test the prototype system. The results obtained show promise in differentiating between user-authored and AI-generated text. The system design and prototype represent valuable contributions for future research in this area. The solution also provides a novel approach to addressing practical challenges posed by LLMs, particularly in maintaining academic integrity in educational settings.
Authors: Wilson, David, Burnett, Parker, Valacich, Joseph S, Jenkins, Jeff
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7028859463