Being humans, our basic form of interaction with each other is through a “conversation”. In today’s world, we have the technology which enables us to have a conversation with the computers through the power of conversational artificial intelligence(AI) such as Alexa, Google home mini, Cortana and Siri. The goal of my research is to develop causal maps of user-specified problems by automatically generating questions and parsing oral answers through Alexa. Currently participants interested in developing causal models have often done it with the support of a trained facilitator, who elicits concepts and causal relations. New software such as MentalModeler allow tech-savvy participants to independently develop causal models. However, both approaches have limitations. A trained facilitator may easily converse with a participant but may be costly or unavailable. Software such as MentalModeler are free and available anytime, but they do require the participant to draw the network directly. Both approaches may struggle when the map becomes large, as the user may not be told that a seemingly new concept is actually merely a synonym for a concept already present. There is thus an interest in building on the success of personal assistants to develop a voice-activated software that guides users in developing a model through a conversation (like a human facilitator) but is available at any time with no cost (like MentalModeler) and continuously examines the map to avoid typical issues such as synonymy of concepts and will automatically inform the user when a synonym of an existing concept is made, or when concepts related to the main theme that may be used instead of narrowly defined concepts.