AI-Based Feedback in Counselling Competence Training of Prospective Teachers

2025-05-06

AI Summary

This study addresses the subjectivity, latency, and quantification challenges in feedback for pre-service teacher training in home-school consultation. It designs and validates the first multimodal AI feedback system embedded within a training闭环 (closed-loop). Methodologically, the system integrates prosodic, facial micro-expression, postural, and semantic features; employs machine learning modeling and human-AI collaborative annotation to identify dialogue phases, counseling techniques, and quality-related patterns; and incorporates iterative seminar sessions and qualitative interviews. Key contributions include: (1) the first empirical confirmation of significant correlations between nonverbal and paralinguistic features and counseling quality; (2) validation of AI-generated feedback efficacy in enhancing trainees’ self-awareness and reflective practice; and (3) preliminary development of a dialogue analysis model demonstrating cross-context generalizability—laying theoretical and empirical foundations for intelligent educational support systems.

Technology Category

• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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.

Problem

• Enhancing teacher counselling skills using AI feedback
• Analyzing verbal and nonverbal communication in teacher training
• Evaluating student perceptions of AI-based feedback effectiveness

Innovation

• AI analyzes verbal, paraverbal, nonverbal communication
• AI feedback correlates features with conversation quality
• Students perceive AI feedback as positive and helpful