SQUbot: Enhancing Student Support Through a Personalized Chatbot System

Educational institutions commonly receive numerous student requests regarding various services. Given the large population of students in a college, it becomes extremely overwhelming for the staff to address the inquiries of all the students while dealing with multiple administrative tasks at the same time. Furthermore, students often make multiple visits to the university’s administration, make multiple calls, or write emails about their concerns, which makes it difficult to respond to their queries promptly. AI-powered chatbots can act as virtual assistants that promptly help students in addressing their simple and complex queries. Most of the research work has focused on chatbots supporting the English language, and significant improvement is needed for implementing chatbots in the Arabic language. Existing studies supporting the Arabic language have either employed rule-based models or built custom deep learning models for chatbots. Rule-based models lack understanding of diverse contexts, whereas custom-built deep learning models, besides needing huge datasets for effective training, are difficult to integrate with other platforms. In this work, we leverage the services offered by IBM Watson to develop a chatbot that assists university students in both English and Arabic. IBM Watson employs natural language understanding and deep learning techniques to build a robust dialog and offers a more scalable, integrable, and customizable solution for enterprises. The chatbot not only provides information about the university’s general services but also customizes its response based on the individual needs of the students. The chatbot has been deployed at Sultan Qaboos University (SQU), Oman, and tested by the university’s staff and students. User testing shows that the chatbot achieves promising results. This first bilingual AI chatbot at SQU supports English and Arabic and offers secure, personalized services via OTP and student email verification. SQUbot delivers both general and individualized academic support. Pilot testing showed 84.9% intent recognition accuracy. Most unidentified queries were due to dialectal variation or out-of-scope inputs, which were addressed through fallback prompts and dataset refinement.