## 2 Methods and Procedures
### Instruments and Data Collection  
In this study, various versions of the LLama model, including the latest LLama 3.1 (Meta AI, Menlo Park, CA, USA) (8B and 70B), were implemented in a local environment and improved as Version 2. The LLama model, developed by Meta, is a large-scale language model characterized by its lightweight architecture, which enables efficient operation even with relatively limited computational resources. The model was downloaded from the Hugging Face repository in the GGF file format and installed locally. The OLLama platform was employed to facilitate the efficient execution of the model. The parameter configuration and template settings were optimized to enhance output quality, which played a critical role in building an efficient AI service within a local environment. To extend the LLama model into a remote service format, the LangChain framework was utilized, allowing the seamless deployment of the AI model as a web-based service. A LangChain server was established to deploy the LLama model in a web service form through simple commands, configuring the model as a \"Remote Runnable\" to enable external accessibility. Additionally, Ngrok was implemented for port forwarding, providing a fixed domain that allowed external users to access the service. This approach contributed to cost-effective infrastructure development and improved the remote usability of the model. Experimental evaluations were conducted to demonstrate the LLama model's diverse applications. Firstly, in text generation and conversational AI functionalities, the locally hosted LLama model demonstrated its capability to respond to natural language queries swiftly and accurately, exhibiting excellent performance in conversational tasks. Secondly, utilizing LangChain for PDF-based information retrieval, the model accurately extracted and inferred specific sentences within PDF files. Lastly, GPU usage monitoring revealed that the LLama model efficiently utilized GPU resources while performing inference tasks in the local environment. These findings highlight the LLama model's practical viability for efficient deployment in resource-constrained settings and its effectiveness across multiple AI applications, including text generation, information retrieval, and computational resource management (Figure 3). This study was conducted to develop a practical model for AI-based digital textbook implementation in universities, specifically aiming to provide open-source technology solutions that are accessible and free for students who lack external funding. To achieve cost efficiency, LLama and LangChain were employed to extend a local large language model (LLM) into a remote service format. This approach was adopted to deliver a low-cost solution for integrating AI technologies into educational settings, aligning with the goal of enhancing AI accessibility in resource-constrained environments. The decision to utilize LLama instead of OpenAI's ChatGPT was based on Chat GPT's observed limitations. Although powerful, ChatGPT often retrieves excessive data from pre-trained web sources, which can introduce inaccuracies in specialized fields such as assistive technology courses. For instance, the term \"leaf\" as a component of assistive devices could be incorrectly interpreted as \"a tree leaf\", demonstrating a generative AI error. To mitigate this, LLama was configured to incorporate the lecture materials as its primary knowledge base, ensuring that the responses were confined to the content within the provided curriculum. Consequently, this study focused on designing a system in which large language models such as LLama or ChatGPT would generate answers strictly based on uploaded lecture content. To implement this, RAG (Retrieval-Augmented Generation) and vector databases were employed. RAG operates by first retrieving relevant information from an uploaded document (PDF) and then generating a response based on the retrieved data.