Intelligent Agents in Educational Institutions: NEdBOT - NLP-based Chatbot for Administrative Support Using DialogFlow

Abstract

Artificial intelligence (AI)-based chatbot systems have seen increased adaption in the educational domain in recent years owing to increased sophistication in the AI domain. However, most of the communication between students and educational institutions is still performed physically and causes major administrative overhead, especially during the time of admission. Contemporary pattern-matching-based and generative-based chatbots underperform to queries outside a limited scope, grammatically and structurally ambiguous inputs, outliers to pre-defined rule-set, and longer response times for a huge knowledge base. We proposed a NEdBOT-An NLP-based Educational Bot, developed by Natural Language Processing models integrated within the DialogFlow platform utilizing a Retrieval-based approach. We evaluate the developed chatbot on a custom dataset generated for the admissions use case of a prominent university. We used an objective evaluation criterion with real-world users to achieve an intent classification accuracy of 76.8% at an average mean response time of 216.43ms per query and a user-friendliness score of 72% on the System Usability Scale (SUS). The results demonstrate the proposed approach's ability to create robust, reliable, responsive, and user-friendly web-based smart chatbots that are highly scalable with the capability to handle wider scopes and vague inputs with ease.