Optimization of Non-Verbal Information for English Conversation Agents Using Interactive Evolutionary Computation

Authors

  • Yuma SHIMOSAKA Graduate School of Science and Engineering, Kansai University Author
  • Emmanuel AYEDOUN Faculty of Engineering Science, Kansai University Author
  • Masataka TOKUMARU Faculty of Engineering Science, Kansai University Author

DOI:

https://doi.org/10.58459/icce.2024.5008

Abstract

As English becomes increasingly important globally, many agent-based conversation practice environments struggle to maintain learner motivation due to a lack of personalized behavior. This study proposes optimizing a conversational agent's non-verbal cues—such as nodding and voice characteristics—through interactive evolutionary computation to enhance learners' motivation. Participants engaged in role- play scenarios across eight settings, providing feedback after each interaction. The agent's behavior was iteratively optimized, and approximately 90% of participants reported increased willingness to interact, suggesting that personalizing non-verbal behavior can significantly improve motivation in language learners.

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Published

2024-11-25

How to Cite

Optimization of Non-Verbal Information for English Conversation Agents Using Interactive Evolutionary Computation. (2024). International Conference on Computers in Education. https://doi.org/10.58459/icce.2024.5008