Simulation-Based Evaluation of Artificial Intelligence-Integrated Visual Teaching Design for Mental Health Promotion...

Visual teaching design in college music education has attracted increasing attention because of its potential to enhance student engagement and promote mental well-being. However, conventional instructional approaches often lack the adaptability needed to respond to learners' evolving cognitive and emotional states, limiting their effectiveness in supporting both academic achievement and psychological balance. The main aim of this simulation-based study is to develop and evaluate an artificial intelligence-driven framework that integrates visual teaching strategies with graph-based emotional modeling to optimize learning outcomes in college music courses. A novel graph-based emotional curriculum intelligence framework (GECIF) is proposed, representing the learning environment as an interconnected graph of knowledge, activity, and emotional nodes. The framework incorporates graph-based representation learning, dynamic state modeling, and adaptive learning mechanisms to simulate the evolution of knowledge acquisition, engagement, emotional stability, and stress over time. A synthetic dataset comprising interaction logs, emotional indicators, and learning performance variables was generated through a controlled MATLAB-based simulation pipeline to ensure reproducibility. GECIF was evaluated against a conventional non-adaptive baseline model under identical experimental conditions across normalized simulation steps. Performance was assessed using normalized simulation metrics, including knowledge gain, emotional stability, stress reduction, engagement level, and robustness. The proposed framework consistently outperformed the baseline, demonstrating higher simulated knowledge acquisition, improved emotional regulation, greater engagement, reduced simulated stress, and enhanced robustness. Statistical analysis based on mean values, standard deviations, and 95% confidence intervals confirmed consistent improvements across all evaluation metrics. These findings demonstrate the potential of AI-driven, emotion-aware visual teaching strategies for adaptive music education while providing a scalable and reproducible computational framework to support future research in intelligent educational systems.