AI-Assisted Emotion Recognition: Impacts on Mental Health Education and Learning Motivation

This paper proposes an AI-assisted emotion recognition system using multimodal audio-visual features with transformer architecture and an enhanced probabilistic matrix factorization model to provide personalized learning content recommendations for mental health education in students. The system aims to detect students' emotional states in real-time and deliver tailored support to enhance learning motivation and mental well-being.

With the rapid advancements in artificial intelligence (AI) technology, its deployment in the field of education has gained considerable attention, particularly in the context of mental health education. Addressing the mounting academic and social pressures faced by contemporary students necessitates the utilization of cutting-edge techniques to accurately discern their emotional states and deliver customized learning resources. Existing methodologies for mental health education often fall short

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