A conversational intelligent agent for career guidance and counseling
We present the preliminary construction of a mixed initiative conversational intelligent agent designed to provide guidance on career pathway information and available resources for common career-relevant personal problems. We use a single job classification within the United State Navy as a proof of concept. With this system, user input from career guidance sessions is linked via advanced natural language processing techniques to our framework of Navy training and education standards, promotion protocols, and organizational structure, producing feedback on resources and recommendations sen-sitive to user history and stated career goals. In recognition of the variety of personal problems that can impact career progress, the intelligent agent also offers rudimentary "coun-seling". Detection of language related to issues (e.g., sleep deprivation, financial difficulties, substance abuse) triggers targeted dialogues that gather more information, offer tai-lored suggestions, and/or provide referrals to appropriate re-sources such as a human counselor when in-depth counseling is warranted. Sessions occur when sailors initiate them, when performance or progress drops below Navy expectations, or with respect to career milestones (the latter two enabled by integration with career monitoring databases). This software, currently in alpha testing, has the potential to serve as an in-tuitively accessible information hub, engaging and encourag-ing sailors to take ownership of their career paths in the most efficient way possible, benefiting both individuals and the Navy as a whole.
Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019
Hampton, A., Rus, V., Andrasik, F., Nye, B., & Graesser, A. (2019). A conversational intelligent agent for career guidance and counseling. Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference, FLAIRS 2019, 402-407. Retrieved from https://digitalcommons.memphis.edu/facpubs/7232