Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
Academic and student services at Eastern Samar State University–Salcedo Campus traditionally rely on manual processes. These methods frequently lead to response delays and restrict information availability outside of regular operating hours, hindering students' timely access to crucial campus data. This study aimed to design, develop, and evaluate an AI-driven, intelligent chatbot tailored to support student service automation at ESSU-SC, focusing on routine, front-facing queries such as enrollment steps, schedules, and general university policies. Employing a developmental research design, the system utilized Google Dialogflow for Natural Language Processing and integrated AWS services alongside OpenAI APIs to build a dynamic conversation workflow. System interfaces were deployed via official university web platforms and Facebook Messenger. A rigorous evaluation framework was executed across three sequential phases: a Benchmark Test, an Alpha Test, and a Beta Test using the ISO 9126 Quality Model, culminating in a System Usability Scale (SUS) evaluation. Evaluators included system design experts from the faculty, standard university students, and a visually impaired individual to verify accessibility features. The system demonstrated progressive quality enhancements across all metrics throughout the testing lifecycle. The initial Benchmark Test yielded a "Very Good" overall mean score of 3.68, which advanced to an "Excellent" rating of 4.76 during the Alpha Test phase following interface and architectural refinements. The real-world Beta Test achieved a mean score of 4.38 ("Very Good"), showcasing perfect efficiency scores (5.00) in real-time response times and data processing. Furthermore, the SUS yielded a final score of 78, confirming that the application is highly usable, practical, and effective as an inclusive, assistive communication tool. The study successfully validated that the intelligent chatbot efficiently bridges communication barriers, automates repetitive administrative inquiries, and improves service accessibility. Future recommendations include expanding the bot's scope to encompass all university services, introducing multi-language capabilities, and adding advanced accessibility features to support broader inclusion.
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.
Ulysses Chinenyeaka Ebizie· International Journal of Com...· 0 citations
This study introduces WA Daksha, an academic chatbot on WhatsApp, linked with Gemini AI, designed to facilitate intelligent academic service automation inside the Faculty of Engineering at Universitas Negeri Surabaya. The study concentrates on analysis and design at Technology Readiness Level 1-3, where system readiness is evaluated through structural requirement validation and expert-based architectural mapping. This process generates a verified system architecture, knowledge base structure, conversation flow, and use case model. The suggested system employs a hybrid conversational methodology that integrates rule-based routing, knowledge-based retrieval using Retrieval-Augmented Generation, and generative AI answers. The design comprises client, platform integration, application, data, and infrastructure layers to guarantee modularity and scalability. The chatbot offers three primary services: academic information, FAQ support, and AI-generated dynamic inquiries via WhatsApp chat. The knowledge base amalgamates institutional academic data, administrative protocols, and contextual AI processing to enhance response precision and dependability. 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. This research advances the creation of AI-driven academic service assistants in higher education and promotes SDG 4 by enhancing access to inclusive and high-quality education via intelligent digital academic services.
Rizky Basatha, B. Putra, S. A. Alamsyah et al.· E3S Web of Conferences· 0 citations
A reverse engineering technique is presented for extracting a behavioural model of a deployed chatbot through the automated exploration of its functionality via Large Language Models through the automated exploration of its functionality via Large Language Models.
Iván Sotillo del Horno, Alejandro del Pozzo, Esther Guerra et al.· SN Computer Science· 0 citations
The rapid advancement of information technology has driven educational institutions to improve the quality of their information services. MTs Sambong Jaya faces challenges in delivering academic information due to its inactive website, resulting in limited accessibility for students and the community. This study aims to develop a madrasah profile website integrated with a rule-based chatbot to enhance the effectiveness of academic information services. This research employed the Research and Development (R&D) method based on the Borg and Gall model, including needs analysis, planning, development, validation, testing, and evaluation stages. The chatbot was designed using predefined question–answer patterns to provide automated responses to user inquiries. The evaluation results show that expert validation achieved a score of 90% (Very Feasible), Blackbox Testing indicated a 100% success rate for all system functions, and user responses reached 81.71% (Very Good). These findings indicate that the developed system is functional, user-friendly, and capable of improving access to academic information.
Amelia Nurhayati, Sulidar Fitri, H. Rubiani· International Journal Educat...· 0 citations
The integration of Artificial Intelligence (AI) into higher education offers scalable support for students but raises concerns regarding over-reliance, reduced effort, and diminished deep learning. This study introduces 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. The system was deployed in an undergraduate Structured Query Language (SQL) course across three consecutive semesters and evaluated using a mixed-methods design combining interaction logs, pre–post questionnaires (N = 170), and classroom observations. Results indicate high perceived ease of use (M = 4.43) and a moderate but statistically significant increase in trust following exposure (from M = 3.29 to M = 3.58), while AI self-efficacy showed only minor changes. Usage patterns revealed a bifurcated structure, with students engaging in both short troubleshooting interactions and extended tutoring dialogues. Qualitative findings highlight adoption waves, tensions between efficiency and depth, and the sensitivity of trust to system reliability. These findings suggest that curriculum-aligned constraints and hint-first scaffolding can support instructional integration without displacing pedagogical goals. Rather than demonstrating causal learning gains, this study contributes design principles and in-situ evidence for deploying domain-specific AI assistants in technical higher-education contexts.
Or Peretz, Roei Zerahia· International Journal of Inf...· 0 citations
Academic libraries increasingly struggle to provide consistent information services as digital collections grow and user inquiries become more varied. Staff-dependent communication channels such as email and live chat are constrained by working hours, leaving visitors without support during evenings, weekends, and holidays. This study proposes an intelligent text-based chatbot to automate visitor inquiry handling in a university digital library setting. The system employs an Artificial Neural Network (ANN) with a multi-layer Perceptron architecture trained on a corpus of 2,319 labeled samples across 56 intent categories. An Indonesian-language preprocessing pipeline was implemented, consisting of case folding, tokenization, stopword removal, and morphological stemming using the Sastrawi algorithm, followed by Bag-of-Words feature extraction producing 1,494-dimensional vectors. The system was deployed using a decoupled architecture, separating the Python Flask inference backend from a PHP CodeIgniter 4 frontend via an Ngrok HTTPS tunnel. Using an 80:20 stratified train-test split, the model achieved a classification accuracy of 91.00%, with a macro-average precision of 0.90 and recall of 0.92 on 464 test samples. Black-box functional testing confirmed stable real-time performance with response latency under three seconds. These results demonstrate that ANN-based intent classification can effectively reduce reliance on manual library staff for routine inquiries while providing continuous 24/7 automated support.