## 2 Methods and Procedures
### Instruments and Data Collection
The implementation process began with PDF preprocessing, where PDF files were converted into text and segmented into smaller chunks. In the next step, these text chunks were transformed into vector embeddings and stored in a vector database such as Pinecone or ChromaDB. Tools such as Sentence-BERT and FAISS were applied to generate embeddings. Figure 3: Example architecture of a FastAPI-Based Multi-Chain Integration System. During the query stage, user questions were converted into vectors, and the system searched for the most relevant chunks in the vector database. Finally, the retrieved text was fed into the LLama model, which generated answers strictly based on the retrieved content. The tools and frameworks used in the implementation included LangChain (an LLM automation library), HuggingFace Transformers, and vector databases such as Pinecone and ChromaDB. This approach ensured that the participants could access accurate and curriculum-specific information while reducing the risk of erroneous interpretations. The designed system successfully prioritized precision and relevance, allowing the students to extract reliable knowledge from the uploaded educational materials. For the study, a Korean text-generation model was essential due to the nature of the research, requiring an autoregressive language model capable of producing natural Korean sentences. For this purpose, the Ko-PlatYi-6B model was employed. Ko-PlatYi-6B is an autoregressive language model designed specifically for Korean text generation, based on the Yi-34B Transformer architecture. The model builds on beomi's Yi-Ko-6B and is further trained on the KOR-OpenOrca-Platypus-v3 dataset by kyujinpy (Figure 4). The model's performance was evaluated across multiple benchmarks, achieving an average score of 49.97. Detailed results include scores of 43.00 on ARC, 53.55 on HellaSwag, 46.50 on MMLU, 40.31 on TruthfulQA, and 66.47 on CommonGen-V2. The model was implemented using PyTorch (Meta AI, USA) and the Transformers library and is available on the Hugging Face Hub. Its CC BY-NC-SA 4.0 license allows free use for non-commercial purposes, making it an ideal choice for integration into this research. Figure 4: User interface for chat and file upload integration using Gradio. The rapid advancements in large language models (LLMs) have led to an increased demand for cloud-based AI services. However, the high cost of such services remains a significant barrier for individual developers and small enterprises. To address this issue and provide students with opportunities to learn and experiment without incurring expenses, LLama models were deployed locally. Furthermore, using LangChain, these models were extended into a remote service format, offering a cost-effective and scalable solution for educational settings. The experimental environment for this study was configured with the Ubuntu 6.8.0-47-generic operating system, a 13 th generation Intel Core i9-13900K, CPU with 94 Gi of memory, and two NVIDIA GeForce RTX 4090 GPUs. This robust setup facilitated efficient model execution, enabling the students to access powerful AI capabilities in a low-cost, resource-optimized environment (Table 2). The AI-Powered Intelligent Textbook System enables students to engage in personalized, self-directed learning that is tailored to their individual preferences, proficiency levels, and academic goals. Through the integration of advanced large language models (LLMs), such as LLama, the platform allows students to upload course materials (e.g., PDFs or text files) and interact with them dynamically. This process facilitates precise content extraction, targeted explanations, and context-specific feedback, empowering learners to navigate course content at their own pace and level of understanding. The process begins with the selection of appropriate model parameters, such as the desired text length, report language, and complexity level, ensuring that outputs meet specific academic requirements.