Abstract
In many social professions employees require skills in affect- and situation-aware social interaction. One option for teaching and training such social interaction skills by computer-based training methodology is the use of dialogue simulations. Here, a student interacts with a simulated dialogue partner and the dialogue flow explores specific interaction situations and affectual settings. Conversational agents provide a basic technology for creating such dialogue simulations. However, they usually lack a means for managing affect-related dialogue state. In this paper we propose an approach to integrate affective reasoning into a conversational agent for intelligent tutoring applications in order to improve the agent’s ability to recognise dialogue intents, generate emotionally aligned responses, and provide a metric for evaluating student performance.
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Acknowledgments
This project is funded by the European Social Fund (ESF) through the Excellence Initiative of the State Mecklenburg-Vorpommern (grant number: ESF/14-BM-A55-0020/19). We thank our domain experts from HS Neubrandenburg and DZNE for their invaluable contribution in providing real world data for developing the dialogue models.
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Abuazizeh, M., Yordanova, K., Kirste, T. (2021). Affect-Aware Conversational Agent for Intelligent Tutoring of Students in Nursing Subjects. In: Cristea, A.I., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2021. Lecture Notes in Computer Science(), vol 12677. Springer, Cham. https://doi.org/10.1007/978-3-030-80421-3_54
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DOI: https://doi.org/10.1007/978-3-030-80421-3_54
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