Abstract:
This study aims to develop an AI-based system for providing accurate and valid career path recommendations. We examine whether AI can enhance career decision-making by determining the effectiveness of an AI chatbot system in offering relevant and trusted career recommendations based on an individual’s personality and hobbies. The AI system will feature a chatbot that acts as a career advisor, interacting with users to gather information using the Myer-Briggs Type Indicator (Quenk, 2000) and suggesting potential career paths.
Users will explore suggested careers and evaluate the alignment with their interests and aspirations. The study hypothesizes that the AI system will deliver accurate and trusted career suggestions due to its ability to process complex information and engage users with its conversational persona (Zhang et al., 2018). The system’s effectiveness will be evaluated through an experiment involving 60 participants—40 high school students and 20 individuals out of school. During the interaction phase, various measurements will be recorded, and post-interaction questionnaires will gather data on users’ experiences.
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018). Personalizing dialogue agents: I have a dog, do you have pets too? In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2204-2213). Association for Computational Linguistics.
Quenk, N. L. (2000). Essentials of Myers-Briggs Type Indicator Assessment. New York: Wiley.
Users will explore suggested careers and evaluate the alignment with their interests and aspirations. The study hypothesizes that the AI system will deliver accurate and trusted career suggestions due to its ability to process complex information and engage users with its conversational persona (Zhang et al., 2018). The system’s effectiveness will be evaluated through an experiment involving 60 participants—40 high school students and 20 individuals out of school. During the interaction phase, various measurements will be recorded, and post-interaction questionnaires will gather data on users’ experiences.
Zhang, S., Dinan, E., Urbanek, J., Szlam, A., Kiela, D., & Weston, J. (2018). Personalizing dialogue agents: I have a dog, do you have pets too? In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2204-2213). Association for Computational Linguistics.
Quenk, N. L. (2000). Essentials of Myers-Briggs Type Indicator Assessment. New York: Wiley.