University students frequently encounter challenges in retrieving relevant academic information due to the limitations of traditional search engines. This research introduces KnowledgePilot, the first 1-bit Large Language Model (LLM) specifically designed to support university students. Leveraging the BitNet b1.58 architecture, which employs ternary parameterization (-1, 0, 1), KnowledgePilot achieves high performance with reduced computational costs, making it both resource-efficient and fast. The system integrates Retrieval Augmented Generation (RAG) pipelines, enabling it to access external academic data sources, thus minimizing hallucination issues common in LLMs and providing accurate, context-specific responses. The research also encompasses the development of tools for file conversion, dataset creation, model pretraining and fine tuning. Comprehensive evaluations will measure the system’s performance and user satisfaction, demonstrating its potential to significantly enhance student access to academic resources, while setting the stage for future advancements in low-bit AI technologies for education.