Jul 2026· JOIV: International Journal on Informatics Visualization· 0 citations
TL;DR
The findings indicate that integrating semantic retrieval with AI-based fallback mechanisms can improve response quality, scalability, and contextual relevance in academic library services.
Abstract
Academic libraries increasingly require intelligent digital services to improve information accessibility, responsiveness, and service efficiency. However, conventional FAQ-based chatbots often suffer from limited semantic understanding, while fully AI-based systems may introduce higher latency and inconsistent institutional responses. This study aims to design and evaluate a hybrid web-based library chatbot integrating a local MySQL FAQ database with OpenAI API services to support digital library services at Universitas Negeri Padang (UNP). The system was developed using a hybrid architecture combining semantic FAQ retrieval and AI-based fallback generation. The implementation utilized HTML, CSS, JavaScript, Node.js, and MySQL, while Bahasa Indonesia Natural Language Processing (NLP) techniques, including normalization, tokenization, stopword removal, and stemming, were applied to improve semantic query matching. Evaluation was conducted using 100 library-related queries, validated by three experts: senior librarians and an information systems lecturer. Experimental results demonstrated that the proposed hybrid chatbot achieved 92% response correctness accuracy with a 95% confidence interval ranging from 86.7% to 97.3%. Routing evaluation also confirmed effective query classification between local FAQ retrieval and AI fallback mechanisms, while benchmarking experiments showed that the proposed architecture outperformed keyword-based FAQ systems, semantic retrieval-only models, and pure LLM-based chatbots in terms of response accuracy and balanced response latency. The findings indicate that integrating semantic retrieval with AI-based fallback mechanisms can improve response quality, scalability, and contextual relevance in academic library services. Future research may focus on adaptive threshold optimization, multilingual support, and domain-specific prompt engineering to further enhance routing precision and the generation of contextual responses.
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 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
This paper explores the practical implementation of a Chatbot designed to connect natural language communication with database interactions by generating Structured Query Language (SQL) queries. Utilizing the OpenAPI framework, a widely-used specification for building APIs, our approach aims to enhance the flexibility and interoperability of the ChatBot. The system not only interprets user queries expressed in natural language but also transforms them into syntactically correct and semantically meaningful SQL commands. This paper provides an overview of the current landscape of natural language processing (NLP) Chatbots, discusses the architecture and design considerations of our implementation, and highlights the methodology for training and fine-tuning using state-of-the-art NLP models. Real-world use cases are presented to showcase the practical applicability of the Chatbot, demonstrating its ability to handle various complexities inherent in natural language queries. This work contributes to the field by presenting a scalable and robust solution that combines NLP and database interactions through the utilization of OpenAPI, with potential implications for improving human-computer interactions within database systems.
R. Santosa, Hartantya Ainiyatuts Tsaniyah, Yoga Ari Tofan et al.· IPTEK: The Journal for Techn...· 0 citations
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.
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
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