A Retrieval-Augmented Generation (RAG)-based chatbot for academic regulation information services using a locally deployed Large Language Model (LLM) and promising feasibility for supporting document-based academic regulation information services is indicated.
Abstract
Academic information services in higher education institutions still face challenges because important information is often distributed through static, lengthy, and difficult-to-navigate PDF regulatory documents. This condition makes it difficult for students to obtain specific academic information efficiently and may increase repetitive inquiries directed to academic administrative staff. This study aims to develop and evaluate a Retrieval-Augmented Generation (RAG)-based chatbot for academic regulation information services using a locally deployed Large Language Model (LLM). The main contribution of this study lies in the implementation of a local RAG architecture for hierarchical academic regulation documents by combining hybrid chunking, hybrid retrieval, reranking, and evaluation using retrieval metrics and RAGAS. The proposed method includes document preprocessing, document segmentation with variations in chunk size and chunk overlap, embedding generation, storage in ChromaDB, hybrid retrieval combining semantic and lexical search, and integration of Gemma 3:4B through Ollama. System evaluation was conducted through six experimental scenarios with different retrieval configurations. Retrieval performance was measured using Precision, Recall, and Mean Reciprocal Rank (MRR), while response quality was evaluated using Faithfulness, Answer Relevancy, Context Precision, and Context Recall. The best configuration was achieved with Top-K 10, chunk size 1024, and chunk overlap 400, producing scores of 0.7950, 0.7868, 0.8093, and 0.8958, respectively. These results indicate promising feasibility for supporting document-based academic regulation information services, although broader validation is still required due to the limited evaluation dataset and restricted document scope.
It is concluded that an NLP- and RAG-based chatbot is feasible to be implemented as a digital campus information service and improves answer accuracy from 58.5% to 89.25%, with an average response time below three seconds.
Zuhri Yanda, Yeni Yanti, Maulinda et al.· IC-ITECHS· 0 citations
Results show that fine-tuning and integrating the BAAI/bge-large model significantly improves the chatbot's ability to retrieve relevant information, demonstrating a significant boost in retrieving the most relevant scholarly information for user queries.
A. Oguntimilehin, Olamide Balogun· Nigerian Journal of Technolo...· 0 citations
Universities now use Large Language Models (LLMs) to transform their processes for managing student information. The paper introduces an upgraded chatbot system for Narasaraopeta Engineering College (NEC) which extends previous on-premise LLM chatbot research by providing four new functions. The system uses (1) Retrieval-Augmented Generation (RAG) to create citation-based responses through LlamaIndex and ChromaDB, (2) Context Memory which maintains conversation flow during multiple dialogue exchanges, (3) Voice Input through OpenAI Whisper Speech-to-Text (STT) technology, and (4) Multilingual Support which covers English and these seven languages: Hindi, Telugu, Tamil, Kannada, and Malayalam through IndicNLP. The system tested 60 benchmark questions across four academic categories which included regulations and examination policies and fee structures and multilingual queries and achieved 96.7% overall accuracy with sub-second text response times and 1.0–1.4 second voice response times. The system operates entirely on-premise through Docker which safeguards institutional data privacy while eliminating the need for recurring cloud API expenses. The upcoming development will create Emotion-Aware AI, FAQ Auto-Learning, Student Portal Integration, and a Mobile Application.
M. Yaswanth, Kopparapu Sai Amar Durgesh, Mogili Harsha Vardhan et al.· 2026 7th International Confe...· 0 citations
The AI chatbot successfully addressed traditional academic information service issues by providing 24/7 service, reducing the workload of campus staff, and ensuring information consistency through RAG technology.
Muhammad Saiful, L. Samsu, Imam Fathurrahman et al.· Infotek : Jurnal Informatika...· 0 citations
This paper presents the design and implementation of intelligent chatbots using Retrieval-Augmented Generation architecture to derive accurate, context-based responses from domain-specific knowledge sources such as PDFs and CSV files. The proposed system integrates information retrieval mechanisms with large language models to overcome crucial limitations of traditional chatbots: hallucination, knowledge cut-off, and inability to access private organizational data. The proposed architecture uses a six-stage pipeline consisting of document ingestion, text splitting, and embedding generation with sentence transformers, semantic retrieval, and response generation via Large Language Models (LLMs). By preprocessing the PDF and CSV documents using a RecursiveCharacterTextSplitter and converting them into 384-dimensional semantic embeddings, the system works via a similarity-based retrieval of document chunks. For implementation, the LlamaIndex framework was used for data indexing and query orchestration, and HuggingFace Transformers for language modeling. Experimental validation conducted on the smart campus chatbot system demonstrated significant improvements in the accuracy, relevance and transparency of responses across education, student services, and administrative support domains compared with a standalone language model. Because of its modular design, it supports a wide range of domain-specific topics without the combinatorial costs of re-training. This is another important application for systems that require regular updates and accurate information. Overall, the proposed framework provides a simple and practical approach for easily implementing domain-aware chatbots.
Varsha Spoorthi Shree Narravula, V. K, Yashaswini B. Suresh et al.· 2026 7th International Confe...· 0 citations
Artificial intelligence has improved greatly and its demand in the education field has increased. To satisfy this demand, this research paper proposes an intelligent tutoring system which uses artificial intelligence techniques like Retrieval-Augmented Generation and Large Language Models. It aims to provide context-relevant and personalized help to students in the learning process. The system has a 3-layer architecture. The frontend layer is managed by React for smooth user interaction. The backend layer is handled by FastAPI for proper processing and response generation and the database layer is managed by ChromaDB, a vector database, which handles proper document storage and data retrieval. Students can upload their study materials such as textbooks, notes, etc. in various formats like PDF, DOCX, TXT. Then, the system processes the documents by recursively splitting text and generating embeddings to convert unstructured content into a proper structured knowledge base. After this, Retrieval-Augmented Generation helps retrieve or fetch the most relevant embeddings and combine them with user’s queries to generate contextually correct answers, based on user upload documents. This helps reduce hallucinations, which is the main aim. The system can also automatically generate flashcards and quizzes with adjustable difficulty. To evaluate the system’s performance, different document-based queries were tried and the system had 90% accuracy on average with 20%-30% decrease in hallucination (as compared to other systems). The response time is between 2 to 5 seconds. These results show the benefits of combining Retrieval-Augmented Generation with LLMs.
Azlaan Khan, Sakshi Chandekar, Atharva Baikar et al.· 2026 4th International Confe...· 0 citations
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