Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content

תקציר מקורי באנגליתarXiv:2607.20446v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in whether LLMs themselves can be used to distinguish AI-generated work from human-authored submissions. In this paper, we investigate the extent to which LLMs can detect their own generated content across multiple educational task types, including programming exercises, reflective writing, and short-answer questions. Using authentic student responses and multiple variants of LLM-generated answers, we evaluate detection performance under different prompting strategies and output formats. Our study addresses three research questions: (1) how accurately LLMs can identify their own outputs across ta