Application of a Semantic-Based Conversational Agent to Student Debt Management

This paper presents a novel Semantic-Based Conversational Agent (CA) and its application to benefit students with debt who might otherwise face serious consequences, not only financially, but psychologically. The novel semantic-based CA interprets scripts consisting of natural language sentences by means of a sentence similarity measure. User input is measured semantically against the content of the scripts in order to respond with an appropriate output string. Scripts are organized into contexts consisting of rules possessing a select number of natural language sentences. Such scripting is effortless and alleviates the burden of the traditional pattern-scripted methodologies. The novel semantic-based CA has promising results following interaction with participants focused on the sensitive domain of student debt management an issue of growing concern.