  The increasing reliance on large language models (LLMs) in academic writing
has led to a rise in plagiarism. Existing AI-generated text classifiers have
limited accuracy and often produce false positives. We propose a novel approach
using natural language processing (NLP) techniques, offering quantifiable
metrics at both sentence and document levels for easier interpretation by human
evaluators. Our method employs a multi-faceted approach, generating multiple
paraphrased versions of a given question and inputting them into the LLM to
generate answers. By using a contrastive loss function based on cosine
similarity, we match generated sentences with those from the student's
response. Our approach achieves up to 94% accuracy in classifying human and AI
text, providing a robust and adaptable solution for plagiarism detection in
academic settings. This method improves with LLM advancements, reducing the
need for new model training or reconfiguration, and offers a more transparent
way of evaluating and detecting AI-generated text.
