Cited by 23Artificial Intelligence in EducationEducational Technology and PedagogyCommunication in Education and Healthcare

Artificial intelligence as a feedback provider in practicing public speaking

Open access
Isotalus, Pekka, Eklund, Marja, Karppinen, Karoliina·2025·Communication Teacher

Abstract

Traditionally, feedback is regarded as crucial in the context of learning and teaching public speaking. In the current course, we analyzed the feedback provided to students by an artificial intelligence (AI) coach and students’ learning experiences in developing public-speaking skills via this tool. The MySpeaker Rhetorich speech coach application served as a platform for practicing public speaking within the course. Students found AI-powered feedback to be advantageous and less biased than feedback from their peers. However, although the AI coach offers benefits, it is not without challenges, and currently, solely relying on it as a teaching tool appears inadequate.Course Voluntary AI-powered public-speaking course.Objectives Participants are able to prepare and deliver a speech and gain basic knowledge and understanding about speech composition and delivery. Participants learn to assess their public-speaking skills by receiving feedback from AI and peers and apply their knowledge on public speaking by providing feedback to others.

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Crossref · 10.1080/17404622.2024.2407910

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  • Students in one study reported the feedback from the GenAI coach was more objective and kinder than some of the feedback they received from their classmates.

    “Still, students in one study reported the feedback from the GenAI coach was more objective and kinder than some of the feedback they received from their classmates (Isotalus et al. 2025).”

    From Generative Artificial Intelligence, Formative Epistemic Injustice, and Educators’ Solutions (2026) · cites this paper as Isotalus et al. 2025

    In “Feedback from the AI coach” and “Appraisal” (pages not available), students describe AI feedback as neutral, unbiased, and easier to receive than peer feedback. That supports “more objective,” but the paper does not report that students found it kinder; “Peer feedback” also describes some classmates’ feedback as positive and encouraging. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. Chen, X., Zou, D., Xie, H., & Cheng, G. (2021). Twenty years of personalized language learning: Topic modeling and knowledge mapping. Educational Technology & Society, 24(1), 205-222. https://doi.org/10.30191/ETS.202101_24(1).0016 · DOI

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Authors
Isotalus, P., Eklund, M., & Karppinen, K.
Affiliations
Tampere University (FI)
Corresponding author
Isotalus, Pekka
Venue
Communication Teacher
Published
2024-10-02
Volume
39
Issue
1
Pages
78-85
ISSN
  • 1740-4622 (ISSN-L)
  • 1740-4630
OpenAlex
W4403063148
Cited by
23
References
17
Citations by year
2026: 10 · 2025: 12 · 2024: 1
FWCI
8.67
Type
journal article
Language
en
Open access
Locations
  • Communication Teacher (publishedVersion, cc-by)
  • Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Technology and Pedagogy
  • Social Sciences › Psychology › Social Psychology › Communication in Education and Healthcare
Keywords
artificial intelligence, AI-generated feedback, public speaking, peer feedback, speech delivery, speech composition, student learning experience, speech education

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