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AI-Based Feedback in Counselling Competence Training of Prospective Teachers

Tobias Hallmen, Kathrin Gietl, Karoline Hillesheim, Moritz Bauermann, Annemarie Friedrich, Elisabeth André

TL;DR

The study demonstrates that AI-based feedback, grounded in multimodal analysis of verbal, paraverbal, and nonverbal cues, can enhance counselling competencies in prospective teachers. By integrating NOVA/Discover-based annotations with WhisperX transcriptions and feature extraction, the authors create a multimodal feedback suite (tabular data, parallel coordinates, radar charts, NOVA playback) embedded in an iterative block seminar. Results show significant correlations between nonverbal/paraverbal features and conversation quality, and generally positive student reception of AI feedback, though verbal feature annotation requires further robustness. The work highlights the potential to augment teacher training with objective, actionable insights while acknowledging the need for larger datasets and automated explanatory mechanisms to strengthen interpretability and scalability.

Abstract

This study explores the use of AI-based feedback to enhance the counselling competence of prospective teachers. An iterative block seminar was designed, incorporating theoretical foundations, practical applications, and AI tools for analysing verbal, paraverbal, and nonverbal communication. The seminar included recorded simulated teacher-parent conversations, followed by AI-based feedback and qualitative interviews with students. The study investigated correlations between communication characteristics and conversation quality, student perceptions of AI-based feedback, and the training of AI models to identify conversation phases and techniques. Results indicated significant correlations between nonverbal and paraverbal features and conversation quality, and students positively perceived the AI feedback. The findings suggest that AI-based feedback can provide objective, actionable insights to improve teacher training programs. Future work will focus on refining verbal skill annotations, expanding the dataset, and exploring additional features to enhance the feedback system.

AI-Based Feedback in Counselling Competence Training of Prospective Teachers

TL;DR

The study demonstrates that AI-based feedback, grounded in multimodal analysis of verbal, paraverbal, and nonverbal cues, can enhance counselling competencies in prospective teachers. By integrating NOVA/Discover-based annotations with WhisperX transcriptions and feature extraction, the authors create a multimodal feedback suite (tabular data, parallel coordinates, radar charts, NOVA playback) embedded in an iterative block seminar. Results show significant correlations between nonverbal/paraverbal features and conversation quality, and generally positive student reception of AI feedback, though verbal feature annotation requires further robustness. The work highlights the potential to augment teacher training with objective, actionable insights while acknowledging the need for larger datasets and automated explanatory mechanisms to strengthen interpretability and scalability.

Abstract

This study explores the use of AI-based feedback to enhance the counselling competence of prospective teachers. An iterative block seminar was designed, incorporating theoretical foundations, practical applications, and AI tools for analysing verbal, paraverbal, and nonverbal communication. The seminar included recorded simulated teacher-parent conversations, followed by AI-based feedback and qualitative interviews with students. The study investigated correlations between communication characteristics and conversation quality, student perceptions of AI-based feedback, and the training of AI models to identify conversation phases and techniques. Results indicated significant correlations between nonverbal and paraverbal features and conversation quality, and students positively perceived the AI feedback. The findings suggest that AI-based feedback can provide objective, actionable insights to improve teacher training programs. Future work will focus on refining verbal skill annotations, expanding the dataset, and exploring additional features to enhance the feedback system.
Paper Structure (17 sections, 1 equation, 5 figures, 3 tables)

This paper contains 17 sections, 1 equation, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Dataset
  4. Methodology
  5. Feature Selection
  6. Feature Extraction
  7. Preparing AI-based Feedback
  8. Evaluation
  9. Qualitative User Study
  10. AI-based Feedback
  11. (i) Tabular Data
  12. (ii) Parallel Coordinates
  13. (iii) Radar Charts
  14. (iv) NOVA Play Back
  15. Students' Feedback
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Exemplary picture of a simulated counselling in NOVA.
  • Figure 2: Coincidence matrix for annotation of conversational phases.
  • Figure 3: Coincidence matrix for annotation of communication techniques.
  • Figure 4: Collective feedback with each line resembling a student and showing the session averages on each axis.
  • Figure 5: Exemplary radar chart showing individual feedback.