Investigating generative AI models and detection techniques: impacts of tokenization and dataset size on identification of AI-generated text

This paper investigates methods for detecting AI-generated text in K-12 student writing assessments using classical machine learning and large language models, comparing outputs from ChatGPT, Claude, and Gemini, and examining the effectiveness of paraphrasing tools like GPT-Humanizer and QuillBot in evading detection.

Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K–12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completing assignments, assessments, or research papers. Current d

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