ChEdBot: Designing a Domain-Specific Conversational Agent in a Simulational Learning Environment Using LLMs

Authors

  • Andreas Martin FHNW University of Applied Sciences and Arts Northwestern Switzerland, Intelligent Information Systems Research Group, Riggenbachstrasse 16, 4600 Olten, Switzerland
  • Charuta Pande FHNW University of Applied Sciences and Arts Northwestern Switzerland, Intelligent Information Systems Research Group, Riggenbachstrasse 16, 4600 Olten, Switzerland
  • Hans Friedrich Witschel FHNW University of Applied Sciences and Arts Northwestern Switzerland, Intelligent Information Systems Research Group, Riggenbachstrasse 16, 4600 Olten, Switzerland
  • Judith Mathez FHNW University of Applied Sciences and Arts Northwestern Switzerland, Institut Weiterbildung und Beratung, Bahnhofstrasse 6, 5210 Windisch, Switzerland

DOI:

https://doi.org/10.1609/aaaiss.v3i1.31198

Keywords:

LLMs, Conversational AI, Chatbot, Education, Simulation

Abstract

We propose conversational agents as a means to simulate expert interviews, integrated into a simulational learning environment: ChEdventure. Designing and developing conversational agents using the existing tools and frameworks requires technical knowledge and a considerable learning curve. Recently, LLMs are being leveraged for their adaptability to different domains and their ability to perform various tasks in a natural, human-like conversational style. In this work, we explore if LLMs can help educators easily create conversational agents for their individual teaching goals. We propose a generalized template-based approach using LLMs that can instantiate conversational agents as an integrable component of teaching and learning activities. We evaluate our approach using prototypes generated from this template and identify guidelines to improve the experience of educators.

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Published

2024-05-20

How to Cite

Martin, A., Pande, C., Witschel, H. F., & Mathez, J. (2024). ChEdBot: Designing a Domain-Specific Conversational Agent in a Simulational Learning Environment Using LLMs. Proceedings of the AAAI Symposium Series, 3(1), 180-187. https://doi.org/10.1609/aaaiss.v3i1.31198

Issue

Section

Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge