AI-Powered University Student Support Chatbot Using AWS Serverless Technologies and Claude AI

Abstract

Artificial Intelligence has boosted the adoption of chatbot systems across higher education, yet many chatbots face limitations. Systems provide shallow or generic responses, struggle with informal or vague student languages, fail to maintain context across multi-turn conversations, and function only as FAQ tools without integration into real university services. To address these challenges, this research aims to design, develop, and evaluate a cloud-native conversational chatbot built on an architecture intended for real-time intelligence. Testing results showed strong intent recognition, accurate data retrieval, effective handling of informal inputs, and reliable multi-turn conversation performance, reducing administrative strain and supporting diverse, complex student inquiries.