Decoding Persuasiveness in Eloquence Competitions: An Investigation into the LLM’s Ability to Assess Public Speaking

The increasing importance of public speaking (PS) skills has fueled the development of automated assessment systems, yet the integration of large language models (LLMs) in this domain remains underexplored.
This study investigates the application of LLMs for assessing PS by predicting persuasiveness. We propose
a novel framework where LLMs evaluate criteria derived from educational literature and feedback from PS
coaches, offering new interpretable textual features. We demonstrate that persuasiveness predictions of a regression model with the new features achieve a Root Mean Squared Error (RMSE) of 0.6, underperforming
approach with hand-crafted lexical features (RMSE 0.51) and outperforming direct zero-shot LLM persuasiveness predictions (RMSE of 0.8). Furthermore, we find that only LLM-evaluated criteria of language level
is predictable from lexical features (F1-score of 0.56), disapproving relations between these features. Based
on our findings, we criticise the abilities of LLMs to analyze PS accurately. To ensure reproducibility and
adaptability to emerging models, all source code and materials are publicly available on GitHub.