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

TL;DR

PresentCoach introduces a dual-agent AI system that creates model presentations and provides interactive, humanized feedback to help learners improve their presentation skills through a cohesive, scalable learning loop.

Contribution

The paper presents a novel dual-agent framework combining exemplar generation and interactive coaching, including audience simulation, for comprehensive presentation skill development.

Findings

System effectively generates high-quality presentation videos from slides.

Coaching feedback aligns with human judgments and improves learner performance.

Audience simulation enhances feedback realism and engagement.

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.