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Abstract

While speech-enabled teachable agents have some advantages over typing-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR). These errors may propagate, resulting in unexpected changes in the flow of conversation. We analyzed how such changes are linked with learning gains and learners’ rapport with the agents. Our results show they are not related to learning gains or rapport, regardless of the types of responses the agents should have returned given the correct input from learners without ASR errors. We also discuss the implications for optimal error-recovery policies for teachable agents that can be drawn from these findings.

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Notes

  1. 1.

    Word error rates were not correlated with rapport (\(r=.196\), \(p=.239\)), learning (\(\rho =.246\), \(p=.142\)), or overall dialogue misrecognition (\(r=-.155\), \(p=.354\)).

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Acknowledgments

We would like to thank anonymous reviewers for their thoughtful comments on this paper. This work was supported by Grant No. 2024645 from the National Science Foundation, Grant No. 220020483 from the James S. McDonnell Foundation, and a University of Pittsburgh Learning Research and Development Center internal award.

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Correspondence to Yuya Asano .

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Asano, Y. et al. (2023). Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport. In: Wang, N., Rebolledo-Mendez, G., Dimitrova, V., Matsuda, N., Santos, O.C. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2023. Communications in Computer and Information Science, vol 1831. Springer, Cham. https://doi.org/10.1007/978-3-031-36336-8_94

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  • DOI: https://doi.org/10.1007/978-3-031-36336-8_94

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