CollegeBot: A Conversational AI Approach to Help Students Navigate College
HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence
In an organization as big as a university that has many distinct departments and administrative bodies, it becomes almost impossible to easily obtain information online or by other means. Assistance over the phone or in-person is often limited to office hours and the information online is scattered through numerous (often nested) web pages, often independently administered and maintained by each sub-division. In this work, we present CollegeBot, a conversational AI agent that uses natural language processing and machine learning to assist visitors of a university’s web site in easily locating information related to their queries. We discuss how we create the knowledge base by collecting and appropriately preprocessing information that is used to train the conversational agent for answering domain-specific questions. We have evaluated two different algorithms for training the conversational model for the chatbot, namely a semantic similarity model and a deep learning one leveraging Sequence-to-Sequence learning model. The proposed system is able to capture the user’s intent and switch context appropriately. It also leverages the open source AIML chatbot ALICE to answer any generic (non domain-specific) questions. We present a proof-of-concept prototype for San Jose State University, to demonstrate how such an approach can be easily adopted by other academic institutions as well.
Chatbot, Conversational AI, Natural language processing, Deep learning, Sequence-to-Sequence, AIML, Semantic sentence similarity.
Mohinish Daswani, Kavina Desai, Mili Patel, Reeya Vani, and Magdalini Eirinaki. "CollegeBot: A Conversational AI Approach to Help Students Navigate College" HCI International 2020 - Late Breaking Papers: Multimodality and Intelligence (2020). https://doi.org/10.1007/978-3-030-60117-1_4