AI-Based Feedback in Counselling Competence Training of Prospective Teachers
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913823069306880 |
|---|---|
| author | Hallmen, Tobias Gietl, Kathrin Hillesheim, Karoline Bauermann, Moritz Friedrich, Annemarie André, Elisabeth |
| author_facet | Hallmen, Tobias Gietl, Kathrin Hillesheim, Karoline Bauermann, Moritz Friedrich, Annemarie André, Elisabeth |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_03423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Based Feedback in Counselling Competence Training of Prospective Teachers Hallmen, Tobias Gietl, Kathrin Hillesheim, Karoline Bauermann, Moritz Friedrich, Annemarie André, Elisabeth Human-Computer Interaction 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. |
| title | AI-Based Feedback in Counselling Competence Training of Prospective Teachers |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.03423 |