Jul 2026· Infotek : Jurnal Informatika dan Teknologi· Vol 9, pp. 604-614· 0 citations
TL;DR
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.
Abstract
Academic information services in higher education institutions still face various obstacles, such as delays in information delivery, limited access to services, and high administrative burdens due to repetitive student inquiries. This study aims to implement Retrieval-Augmented Generation (RAG) and Semantic Search technology in a web-based Artificial Intelligence chatbot to optimize academic services at the Faculty of Engineering, Hamzanwadi University. The research method used is Design Science Research (DSR), which includes data collection, system requirements analysis, design, implementation, testing, and system evaluation. The chatbot's knowledge base is built from academic documents such as academic guidelines, service SOPs, academic calendars, scholarship information, and other administrative documents. The system was developed using an integration of LangChain, Azure OpenAI Service, Azure AI Search, FastAPI, Next.js, and Supabase. Semantic Search techniques are used to perform vector embedding-based searches, while RAG is utilized to generate contextual answers based on relevant documents. Test results show that all key system features performed well with a 100% success rate in unit testing. A user satisfaction evaluation using the Customer Satisfaction Index (CSI) method with 54 respondents yielded a score of 89.34%, categorized as "Very Satisfied," with a Mean Satisfaction Score above 4.37 on a maximum scale of 5.0. 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.
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.
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
The findings indicate that integrating semantic retrieval with AI-based fallback mechanisms can improve response quality, scalability, and contextual relevance in academic library services.
Asrul Huda, Ella Dwipujana Asyani, Firdaus Firdaus et al.· JOIV: International Journal...· 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
The architectural evaluation demonstrates that WA Daksha provides an accessible and scalable framework for automating realtime academic services, facilitating multi-user interaction among students, lecturers, and the public.
Rizky Basatha, B. Putra, S. A. Alamsyah et al.· E3S Web of Conferences· 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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