An Evaluation of AI-Supported Evidence-Based Learning for Public Speaking Skill Development

Abstract:Public speaking is an essential skill in academic and professional contexts, but it often causes anxiety. Although several AI-based speech coaching tools exist, they typically provide generic feedback and lack model speeches for learning. This study addresses these gaps by developing a system that incorporates speech context into evaluation criteria, enabling more tailored feedback. It also generates improved model speeches in both text and audio formats to support example-based learning. The system was evaluated in a pilot study with seven undergraduate students over one to three weeks. Participants used the system repeatedly and practiced the same speech at least three times. Results showed improvements in public speaking performance for all participants. Filler word usage decreased, and anxiety levels dropped by 15.3% to 34.4%. Participants found the context-aware feedback and revised speeches useful and confidence-building. These findings suggest that context-aware feedback and model speeches can enhance public speaking skills and reduce anxiety. Future studies could explore video-based analysis of physical behavior during speeches.

Computation and Language (cs.CL)

arXiv:2609.31676 [cs.CL]

(or arXiv:2609.31676v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.31676 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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