InCoRe -- An Interactive Co-Regulation Model: Training Teacher Communication Skills in Demanding Classroom Situations

2025-02-27

AI Summary

This paper addresses the critical challenge of insufficient emotion regulation and communication skills among teachers in high-stakes classroom settings. Methodologically, it introduces the first interactive co-regulation training model explicitly designed to enhance teacher–student relational dynamics: (1) it pioneers the adaptation of caregiver–child co-regulation theory to educational contexts; (2) it develops a psychologically grounded virtual student framework integrating classroom management principles and affective modeling; and (3) it establishes a behavior annotation system capturing teachers’ subjective psychological experiences. Technically, the system integrates psychological modeling, virtual agent simulation, multimodal behavioral analysis, and a hybrid AI architecture to yield an interpretable, trainable co-regulation intervention platform. Empirical validation confirms a significant causal link between teachers’ emotional experience and their classroom regulatory behaviors—providing both theoretical grounding and an evidence-based implementation paradigm for educational AI, thereby filling a key gap in empirically validated affective-intelligence interventions for teaching.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Emotional Intelligence
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Socioemotional and regulation processes in learning are important. We add to the understanding of previous work on co-regulation processes in the learning sciences, extending the caregiver-child paradigm and focusing on the teacher-student relation by presenting an interactive co-regulation model and the methodology for developing empirically grounded systems for training teachers. We focus on the combination of classroom management and affect models and detail the use of a psychological model to operationalise and automate the interaction with the virtual student. We delve into an annotation scheme developed to capture teacher subjective psychological experiences during training and how these affect their co-regulation behavior with students and contributes to understanding the role of teacher emotional experiences and their consequences of co-regulation processes for classroom management. This research is also a contribution to developing hybrid AI systems.

Problem

• Develops teacher communication training model
• Combines classroom management and affect models
• Explores teacher emotional experiences in co-regulation

Innovation

• Interactive co-regulation model
• Psychological model automation
• Annotation scheme development