E-MOTE: A Conceptual Framework for Emotion-Aware Teacher Training Integrating FACS, AI and VR

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Neuroscience in Education

Background:

• Teacher education currently lacks robust methods for developing emotional awareness and perceptual micro-skills.
• Existing training often relies on macro-behavioral simulations, neglecting nuanced emotional perception.
• Neuroscientific and educational research highlights the importance of emotional competencies for effective teaching.

Purpose of the Study:

• To propose E-MOTE (Emotion-aware Teacher Education Framework), a conceptual model for enhancing teacher education.
• To outline an integrated AI-FACS-VR architecture for real-time and post hoc feedback on teacher perception.
• To establish a foundation for empirical validation of emotion-aware teacher training.

Main Methods:

• Conceptual model development based on neuroscientific and educational research.
• Integration of Facial Action Coding System (FACS), Artificial Intelligence (AI), and Virtual Reality (VR).
• Design of a staged experimental blueprint for ethical validation.

Main Results:

• Clarification of the multi-layered construct of emotion-aware teaching (detection, perception, awareness, regulation).
• Proposal of an integrated AI-FACS-VR architecture for feedback on perceptual performance.
• Outline of a framework for cultivating perceptual micro-skills in teacher practice.

Conclusions:

• E-MOTE provides a structured blueprint for developing emotion-aware teacher training environments.
• The framework supports the cultivation of emotionally responsive pedagogy and inclusive classroom management.
• This model shifts training towards micro-skill development using AI-FACS integration.