GenAI Supports the Research on the Key Factors and Intervention Strategies of Mental Health Resilience Construction of Normal School Students

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Authors: Wendong Chen
Cited by: 0

Abstract

As future educators, normal students' mental health resilience is directly related to the quality of education and teaching and students' development.With the rapid development of artificial intelligence technology, GenAI provides a new technical path for mental health intervention.Focusing on normal university students, this study explored the key mechanisms and effective intervention strategies of GenAI technology in the cultivation of psychological resilience through mixed research methods.The study found that a personalized counseling program, intelligent emotion recognition and instant feedback constituted the three core elements of GenAI support, and these elements significantly improved the intervention effect through technical means such as intelligent dialogue and virtual situation simulation.Based on empirical data analysis, a hierarchical intervention model was proposed: primary intervention focused on daily stress counseling, intermediate intervention strengthened frustration coping ability, and advanced intervention cultivated occupational psychological resilience.The practice showed that the intervention strategy integrated with GenAI could significantly improve the emotional regulation ability and social adjustment level of normal students, especially in the professional identity and teaching efficacy.The research results provide an operational implementation framework for mental health education in normal universities, and have important reference value for the construction of an intelligent psychological support system.Future research can further explore the integration of multimodal technology and the optimization of long-term follow-up evaluation mechanism.

Fields of Study & Subjects

Artificial Intelligence in Education Student Stress and Coping Digital Mental Health Interventions

Publication details

Journal / source
OALib
Volume
13
Issue
01
Pages
1–17
Publication date
1 January 2026