Advancements in Natural Language Understanding and Applications: A Study on ChatGPT with Novel Approaches to Fairness and Accuracy in Education

Natural Language Processing (NLP) has evolved significantly since the 1950s, with early research focusing on tasks like machine translation, information retrieval, and text summarization. Initially, most NLP research emphasized syntactic analysis, but the challenge of understanding and processing unstructured text remained. Recent advancements in large-scale language models (LLMs), such as ChatGPT, have revolutionized the field by enabling zero-shot execution of various NLP tasks without task-specific training. ChatGPT, based on the Transformer architecture, represents a major breakthrough in NLP, generating high-quality natural language responses and self-correcting based on dialogue context. This paper investigates the applications of ChatGPT in educational tasks, focusing on enhancing the fairness and accuracy of its responses. Fairness refers to ensuring that users receive consistent and unbiased answers to the same queries, while accuracy involves improving the precision of ChatGPT's outputs, particularly in complex educational tasks such as scientific literature review and content comprehension. We propose novel solutions, including dynamic model weighting for fairness and integrating external knowledge bases to boost accuracy. Experiments conducted on educational tasks demonstrate significant improvements in both fairness and accuracy, showing that the proposed methods enable ChatGPT to provide more reliable and consistent educational support. Our findings suggest that, with these innovations, ChatGPT can play a pivotal role in improving educational outcomes by offering fair access to quality learning content while addressing accuracy challenges. Future work will explore expanding this approach to other fields where NLP plays a crucial role.