10. Development path of informatisation of psychological nursing education on emotional disorders based on aigc assistance research

Abstract Background The quality of psychological nursing care for patients with mood disorders (MD) largely depends on caregivers' emotional assessment skills, communication abilities, and crisis identification capabilities. However, traditional psychological nursing education models exhibit significant limitations in resource allocation, practical scenario development, and individualized training. With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technology, it demonstrates unique advantages in simulating emotional scenarios, generating diverse patient feedback, and providing real-time instructional support. Although AIGC applications in medical education are expanding, systematic research remains lacking regarding its development pathways, effective mechanisms, and implementation strategies in psychological nursing education informatization. Methods To establish an AIGC-assisted framework for digitalized psychological nursing education and explore its digital development pathways for cultivating nursing competencies in emotional disorders, this study employed a mixed-method approach comprising expert interviews (n = 18), scenario-based teaching experiments (involving 162 nursing students), and system requirements analysis. The AIGC system generated emotion fluctuation cases, crisis dialogue scripts, and personalized learning feedback. Participants were randomly assigned to an experimental group (n = 82) or control group (n = 80) based on class assignments. Both groups showed no statistically significant differences in baseline characteristics including gender, age, and prior course foundations, ensuring comparable teaching interventions. The experimental group utilized an AIGC-supported digital learning platform, while the control group received traditional offline psychological nursing instruction. Key evaluation metrics included Mood Assessment Competence (MAC), Psychological Nursing Performance (PNP), and Learning Self-Efficacy Scale (LSES). Differences between groups were analyzed using a mixed-effects model, with thematic analysis identifying critical pathways for educational digitalization development. Results The experimental group demonstrated a 46% improvement in emotional assessment skills compared to baseline, significantly outperforming the control group's 19% increase (p=.002). Psychological nursing proficiency scores rose by 38% in the experimental group versus 14% in the control group, with statistically significant differences (p=.004). Learning self-efficacy improved by 41% in the experimental group, markedly higher than the control group's 17% increase (p=.006). Further interviews revealed three key benefits of AIGC: 1) Generating highly realistic emotional scenarios to enhance students’ grasp of emotional recognition essentials; 2) Personalizing learning paths through automated feedback; 3) Assisting instructors in developing scalable psychological nursing teaching resources. System requirements analysis identified four critical components for digitalized teaching pathways: content generation quality, ethical standards, bias prevention mechanisms, and teachers' digital literacy. Additionally, learners' platform usage frequency showed a positive correlation with skill improvement (p=.01), indicating that digital engagement significantly impacts training effectiveness. Discussion Research demonstrates that AI-assisted mental health education significantly enhances core competencies in emotional disorder care, particularly in critical skills such as emotional assessment, communication intervention, and crisis identification. Through immersive simulations, precise feedback, and resource scalability, AIGC provides a new technological pathway for digital mental health education. The development of educational digitalization should strike a balance between technological innovation and ethical safeguards, including content credibility, student privacy protection, and standardized application of emotional data. Overall, AIGC offers a viable approach to building intelligent, personalized, and sustainable educational systems for emotional disorder care, laying the foundation for future innovations in mental health education models. Future research could further integrate multimodal emotion recognition technology with generative AI systems to validate AIGC's long-term teaching effectiveness and adaptability in real-world clinical settings.