Aug 2026· International Journal of Computer Science and Mathematical Theory· 0 citations
TL;DR
The study concludes that AI-powered chatbot systems offer a substantive remedy to institutional communication inefficiencies and recommends full deployment alongside continuous model retraining using current institutional data.
Abstract
The rising demand for real-time, automated information services within Nigerian tertiary
institutions has brought the shortcomings of conventional inquiry management systems into sharp
relief. Federal Polytechnic Nekede, Owerri, contends with a persistent backlog of prospective
student queries, admission-related inquiries, and general information requests, particularly
during peak application periods. This study reports the design and implementation of an Artificial
Intelligence (AI)-driven chatbot system, designated NEKBOT, developed specifically for the
institution's admissions and information services. The system employs Natural Language
Processing (NLP), machine learning intent classification, and a structured institutional knowledge
base to generate context-sensitive, accurate, and near-instantaneous responses to user queries.
Development proceeded through the Agile Scrum methodology across six iterative sprints. The
implementation stack comprised Python (Flask framework), Google Dialogflow NLP engine,
JavaScript, and a MySQL relational database. Evaluation outcomes show an intent recognition
accuracy of 91.4%, a mean response latency of 1.83 seconds, and a user satisfaction rating of 87%
on standardised usability measures. The study concludes that AI-powered chatbot systems offer a
substantive remedy to institutional communication inefficiencies and recommends full deployment
alongside continuous model retraining using current institutional data.
The study successfully validated that the intelligent chatbot efficiently bridges communication barriers, automates repetitive administrative inquiries, and improves service accessibility, confirming that the application is highly usable, practical, and effective as an inclusive, assistive communication tool.
Gil G. Dialogo, Hershey Alburo-Abugadie, Enrico C. Lucero· International journal of com...· 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
The design and implementation of an AI-driven check-in agent that automates student onboarding tasks in a university setting is presented, positioning the AI check-in agent as a valuable contribution to smart campus initiatives.
Sahar Bukhari· Journal of Climate Innovatio...· 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
Property information services at Griya Alam Mandiri currently rely on manual WhatsApp interactions during working hours, often leading to delayed responses for prospective buyers. This study aims to implement a Natural Language Processing (NLP) based chatbot using the IndoBERT model on the housing website to automate 24/7 customer service. The methodology involves extracting 3,082 training data samples augmented with typos and informal language, model fine-tuning, and benchmarking against comparative architectures. The evaluation results show that the IndoBERT Base model highly excels in intent classification with an accuracy of 95.85% and an F1-Score of 95.87%, outperforming Indonesian RoBERTa, IndoBERTweet, and XLM-RoBERTa. This system was successfully integrated into a Client-Server architecture connecting a React interface and a FastAPI backend. The final evaluation through User Acceptance Testing (UAT) involving 50 respondents achieved an overall feasibility percentage of 82.50% (Very Satisfied). In conclusion, the IndoBERT chatbot is proven effective in improving the speed of real-time information services and successfully reduces administrative workloads. Future research suggestions include integrating Large Language Models (LLM) with the Retrieval-Augmented Generation (RAG) method to generate more dynamic responses.
Michael, a syllabus-aware AI teaching assistant designed to scaffold reasoning through structured, hint-first dialogue aligned with course progression, rather than providing direct solutions, is introduced, suggesting that curriculum-aligned constraints and hint-first scaffolding can support instructional integration without displacing pedagogical goals.
Or Peretz, Roei Zerahia· International Journal of Inf...· 0 citations
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