Pre-service teachers and public speaking anxiety. Insights and possible interventions through AI and IoT

Public speaking is an essential skill for pre-service teachers as it plays a critical role in both professional success and the teaching-learning process. Despite its importance, many individuals, particularly students, experience public speaking anxiety, which can affect their performance. This paper explores the intersection of self-assessed public speaking anxiety levels and biometric data collected through stress tracker devices and AI-based emotion detection tools, aiming to investigate students’ awareness of their anxiety during public speaking. Data were collected from 257 students enrolled in a first-year course at the University of Macerata (2023/24). Students were asked to complete a survey and give an oral presentation in groups. A subset of 25 student responses were compared with biometric data, including heart rate measurements and data obtained by an AI emotion recognition system. Cross-referencing self-reported anxiety levels with heart rate data showed varying degrees of convergence, suggesting that students often misperceived their emotional state. In some cases, their self-assessments did not align with the biometric indicators of stress, suggesting a lack of self-awareness regarding their anxiety levels. These findings highlight the potential for AI and IoT technologies to provide useful data that can help teachers identify and support students who are not aware of their emotional state and who also have anxiety management issues. By incorporating these tools, university teachers can develop optimized and tailored interventions aimed at improving students’ public speaking skills and emotional regulation. Further research should explore the effectiveness of these tools across different courses and sample sizes.

This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-93999-0_8. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-ma...