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arXiv · 2511.15253

PresentCoach: Dual-Agent Presentation Coaching through Exemplars and Interactive Feedback

Sirui Chen·Jinsong Zhou·Xinli Xu·Xiaoyu Yang·Litao Guo·Ying-Cong Chen
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Abstract

Effective presentation skills are essential in education, professional communication, and public speaking, yet learners often lack access to high-quality exemplars or personalized coaching. Existing AI tools typically provide isolated functionalities such as speech scoring or script generation without integrating reference modeling and interactive feedback into a cohesive learning experience. We introduce a dual-agent system that supports presentation practice through two complementary roles: the Ideal Presentation Agent and the Coach Agent. The Ideal Presentation Agent converts user-provided slides into model presentation videos by combining slide processing, visual-language analysis, narration script generation, personalized voice synthesis, and synchronized video assembly. The Coach Agent then evaluates user-recorded presentations against these exemplars, conducting multimodal speech analysis and delivering structured feedback in an Observation-Impact-Suggestion (OIS) format. To enhance the authenticity of the learning experience, the Coach Agent incorporates an Audience Agent, which simulates the perspective of a human listener and provides humanized feedback reflecting audience reactions and engagement. Together, these agents form a closed loop of observation, practice, and feedback. Implemented on a robust backend with multi-model integration, voice cloning, and error handling mechanisms, the system demonstrates how AI-driven agents can provide engaging, human-centered, and scalable support for presentation skill development in both educational and professional contexts.

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Sirui Chen, Jinsong Zhou, Xinli Xu, Xiaoyu Yang, Litao Guo, Ying-Cong Chen. 2025-11-19. PresentCoach: Dual-Agent Presentation Coaching through Exemplars and Interactive Feedback. https://arxiv.org/abs/2511.15253

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