Article: Enhancing presentation skills: key technical features of automated feedback systems - a systematic feature analysis Journal: International Journal of Technology Enhanced Learning (IJTEL) 2025 Vol.17 No.6 pp.1 - 25 Abstract: Oral presentation automated feedback systems (OPAFs) aim to enhance public speaking by offering automated feedback. Despite their potential, most remain at the prototype stage and are not widely adopted in education. This study systematically analyses the implementation of functional features in existing OPAFs and investigates the evaluation methodologies used in published research. A scoring sheet, developed from expert input and supporting literature, was used to analyse 14 systems across 83 features and 12 additional aspects. Results show a low implementation rate of just 16%, with notable gaps in verbal-nonverbal congruency, adaptive feedback, and content structuring. Evaluation methods often focus on usability and user experience, while learning outcomes and pedagogical value are rarely addressed. Most studies rely on lab-based evaluations, limiting generalisability. The findings underline the need for improved feature integration, real-world testing, and collaboration with educators. Addressing these issues could support the transition from experimental tools to effective educational technologies. Inderscience Publishers - linking academia, business and industry through research

Title: Enhancing presentation skills: key technical features of automated feedback systems - a systematic feature analysis

Authors: Stefan Hummel; Jan Schneider; Nina Mouhammad; Roland Klemke; Daniele Di Mitri

Addresses: DIPF ' Leibniz Institute for Research and Information in Education, Rostocker Str. 6, 60323 Frankfurt am Main, Germany; Goethe University Frankfurt, Theodor-W.-Adorno-Platz 1, 60323 Frankfurt am Main, Germany ' DIPF ' Leibniz Institute for Research and Information in Education, Rostocker Str. 6, 60323 Frankfurt am Main, Germany; Goethe University Frankfurt, Theodor-W.-Adorno-Platz 1, 60323 Frankfurt am Main, Germany ' DIPF ' Leibniz Institute for Research and Information in Education, Rostocker Str. 6, 60323 Frankfurt am Main, Germany; Goethe University Frankfurt, Theodor-W.-Adorno-Platz 1, 60323 Frankfurt am Main, Germany ' TH Köln University of Applied Sciences, Cologne Game Lab, Schanzenstr. 28, 51063 Köln, Germany ' DIPF ' Leibniz Institute for Research and Information in Education, Rostocker Str. 6, 60323 Frankfurt am Main, Germany

Abstract: Oral presentation automated feedback systems (OPAFs) aim to enhance public speaking by offering automated feedback. Despite their potential, most remain at the prototype stage and are not widely adopted in education. This study systematically analyses the implementation of functional features in existing OPAFs and investigates the evaluation methodologies used in published research. A scoring sheet, developed from expert input and supporting literature, was used to analyse 14 systems across 83 features and 12 additional aspects. Results show a low implementation rate of just 16%, with notable gaps in verbal-nonverbal congruency, adaptive feedback, and content structuring. Evaluation methods often focus on usability and user experience, while learning outcomes and pedagogical value are rarely addressed. Most studies rely on lab-based evaluations, limiting generalisability. The findings underline the need for improved feature integration, real-world testing, and collaboration with educators. Addressing these issues could support the transition from experimental tools to effective educational technologies.

Keywords: oral presentation automated feedback systems; OPAFs; public speaking; feature analysis; automated feedback; technology acceptance; system adoption.

DOI: 10.1504/IJTEL.2025.148593

International Journal of Technology Enhanced Learning, 2025 Vol.17 No.6, pp.1 - 25

Received: 28 Apr 2025
Accepted: 21 May 2025
Published online: 14 Sep 2025 *

Title: Enhancing presentation skills: key technical features of automated feedback systems - a systematic feature analysis

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