# Probing Experts' Perspectives on AI-Assisted Public Speaking Training

arXiv: https://arxiv.org/abs/2507.07930 · HTML: https://mlsanity.com/paper/2507.07930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos](https://mlsanity.com/paper/1907.12918/) (2019)
- [VoiceCoach: Interactive Evidence-based Training for Voice Modulation Skills in Public Speaking](https://mlsanity.com/paper/2001.07876/) (2020)
- [GestureLens: Visual Analysis of Gestures in Presentation Videos](https://mlsanity.com/paper/2204.08894/) (2022)
- [Semi-Automated & Collaborative Online Training Module For Improving Communication Skills](https://mlsanity.com/paper/1704.08735/) (2017)
- [SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness](https://mlsanity.com/paper/2309.05091/) (2023)

## Abstract (arXiv)

Background: Public speaking is a vital professional skill, yet it remains a source of significant anxiety for many individuals. Traditional training relies heavily on expert coaching, but recent advances in AI has led to novel types of commercial automated public speaking feedback tools. However, most research has focused on prototypes rather than commercial applications, and little is known about how public speaking experts perceive these tools. Objectives: This study aims to evaluate expert opinions on the efficacy and design of commercial AI-based public speaking training tools and to propose guidelines for their improvement. Methods: The research involved 16 semi-structured interviews and 2 focus groups with public speaking experts. Participants discussed their views on current commercial tools, their potential integration into traditional coaching, and suggestions for enhancing these systems. Results and Conclusions: Experts acknowledged the value of AI tools in handling repetitive, technical aspects of training, allowing coaches to focus on higher-level skills. However they found key issues in current tools, emphasising the need for personalised, understandable, carefully selected feedback and clear instructional design. Overall, they supported a hybrid model combining traditional coaching with AI-supported exercises.