Large Language Models face accuracy and academic integrity challenges

Large language models (LLMs) face challenges regarding reliability and accuracy, often producing incorrect information known as hallucinations because they rely solely on training data. To address this, Retrieval-Augmented Generation (RAG) has been developed. RAG functions by combining a retrieval system that searches a specific knowledge base—such as internal company documents or legal contracts—with a generative model. This allows the AI to 'read' and ground its answers in real-time, verifiable content rather than relying only on its memorized training.

In academic settings, the rise of LLMs like ChatGPT and Claude AI has sparked debate over research misconduct. While AI can assist with research tasks such as identifying topics, summarizing sources, and structural planning, its use in generating actual prose or entire papers is viewed by many scholars as a violation of academic integrity. The distinction between using AI for editorial assistance versus using it for passage or paper generation remains a central concern for educators.