Concept of an AI-assisted Regulations and Student Support Chatbot for the Faculty of Engineering at the University of Mauritius

Abstract

The growing complexity of programme regulations, administrative procedures, and academic requirements within higher education institutions often leaves students struggling to access accurate and timely information. This paper presents the concept of an artificial intelligence (AI)-assisted Regulations and Student Support Chatbot designed specifically for the Faculty of Engineering at the University of Mauritius. The proposed system aims to serve as an accessible digital assistant capable of responding to student queries related to programme regulations, progression rules, assessment policies, submission guidelines, and general administrative processes. Leveraging advances in large language models (LLMs) and retrieval-augmented generation (RAG), the chatbot would integrate faculty-specific documents—such as student handbooks, programme regulations, and official University policies—into a searchable knowledge base to deliver precise and contextually grounded answers.

Beyond providing information, the chatbot is conceptualised as a tool to enhance student success by reducing uncertainty, improving regulatory literacy, and supporting self-directed learning. It aims to complement rather than replace the academic and administrative support structures by streamlining routine interactions. This would allow staff members to focus on higher-value tasks. The paper outlines the system architecture, data preparation workflow, ethical considerations, and proposed deployment strategy. Emphasis is laid on transparency, accuracy, and responsible use.

By addressing information bottlenecks and improving communication between students and the institution, the proposed AI-assisted chatbot will contribute to a more supportive and efficient learning environment. It aims to illustrate how tailored AI solutions can strengthen student engagement, enhance operational efficiency, and support institutional goals in a resource-constrained higher education context.