AI in Business and Society
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Paper Number
2456
Paper Type
Completed
Description
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.
Recommended Citation
Wilson, David; Burnett, Parker; Valacich, Joseph S.; and Jenkins, Jeff, "Human or AI? Using Digital Behavior to Verify Essay Authorship" (2023). ICIS 2023 Proceedings. 6.
https://aisel.aisnet.org/icis2023/aiinbus/aiinbus/6
Human or AI? Using Digital Behavior to Verify Essay Authorship
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.
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