Hybrid conversational AI for intelligent tutoring systems

We present an approach to improve individual and self-regulated learning in group assignments. We focus on supporting individual reflection by providing feedback through a conversational system. Our approach leverages machine learning techniques to recognize concepts in student utterances and combines them with knowledge representation to infer the student’s understanding of an assignment’s cognitive requirements. The conversational agent conducts end-to-end conversations with the students and prompts them to reflect and improve their understanding of an assignment. The conversational agent not only triggers reflection but also encourages explanations for partial solutions.

Keywords

Subject (DDC)

330 - Wirtschaft

Project

Event

AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)

Exhibition start date

Exhibition end date

Conference start date

22.03.2021

Conference end date

24.03.2021

Date of the last check

ISBN

ISSN

Language

English

Created during FHNW affiliation

Yes

Strategic action fields FHNW

Publication status

Published

Review

Peer review of the complete publication

Open access category

Diamond

License

Citation

Pande, C., Witschel, H. F., Martin, A., & Montecchiari, D. (2021). Hybrid conversational AI for intelligent tutoring systems. In A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, & F. v. Harmelen (Eds.), Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021). Sun SITE, Informatik V, RWTH Aachen. https://doi.org/10.26041/fhnw-6988

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