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

2025-11-19

AI Summary

Current AI-based presentation training tools suffer from fragmented functionality, insufficient high-quality exemplars, and a lack of personalized, immersive feedback. To address these limitations, we propose a dual-agent collaborative framework: an Ideal Demonstration Agent generates personalized demonstration videos from input slides, while a Coach Agent delivers structured, multimodal feedback—analyzing speech, visual behavior, and verbal content—using a novel Observation-Impact-Suggestion (OIS) format. A third Audience Agent simulates authentic listener responses to enhance feedback humanization and contextual immersion. The system integrates slide understanding, vision-language modeling, text-to-speech synthesis, voice cloning, and multimodal speech analysis to establish an end-to-end training loop. Experimental results demonstrate significant improvements in user engagement, pedagogical effectiveness, and skill acquisition efficiency. The framework exhibits strong scalability and practical applicability in both educational and professional training settings.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Creating AI coaching with exemplar videos and interactive feedback for presentations
• Integrating multimodal analysis and audience simulation for authentic learning
• Providing scalable personalized presentation training through dual-agent system

Innovation

• Dual-agent system with Ideal Presentation and Coach agents
• Ideal agent creates exemplar videos via multimodal synthesis
• Coach agent evaluates using multimodal analysis and OIS feedback