DEVELOPMENT AND ACCEPTABILITY OF AN INTELLIGENT CHATBOT FOR STUDENT SERVICES IN EASTERN SAMAR STATE UNIVERSITY SALCEDO CAMPUS

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.