2023年4月，教育部等十七部门联合印发《全面加强和改进新时代学生心理健康工作专项行动计划 （2023-2025年）》，着重强调要将心理健康工作置于更为突出的位置，构建全方位心理育人体系，强 化心理健康教育的系统性与协同性，为高校心理健康教育工作明确方向。2024年7月，党的二十届三中 全会通过的《中共中央关于进一步全面深化改革、推进中国式现代化的决定》指出，"健全社会心理服 务体系和危机干预机制"，进一步凸显心理健康工作在国家战略布局中的重要性。而在2025年的政府工 作报告中，明确提出"普及心理健康教育""健全社会心理服务体系和危机干预机制"，为心理健康教育工 作提供了更为清晰的行动指南。 在经济社会快速发展与高等教育普及化的新时代背景下，大学生心理健康问题呈现出"发生率上升、类 型多元化、隐蔽性增强"的特征，学业压力引发的焦虑、就业不确定性带来的迷茫、社交适应障碍催生 的孤独等问题交织，传统"条块分割""各自为战"的心理健康教育模式，已难以满足全面育人需求与学生 多样化心理服务诉求，亟需以系统性思维和整体性布局推进改革。 【作者付建龙系江苏经贸职业技术学院副院长，谢鑫建系江苏经贸职业技术学院学工处处长， ...

Core Viewpoint - The increasing mental health issues among university students due to intensified social competition and changing educational environments necessitate innovative approaches in psychological support, particularly through the integration of Artificial Intelligence (AI) in mental health education [1][10]. AI Empowerment in University Mental Health Education - AI is being integrated into university mental health services through five key areas: intelligent screening, information integration, process intervention, effect evaluation, and psychological literacy enhancement [1]. - Intelligent screening and risk warning systems using AI can improve the accuracy and timeliness of mental health assessments by utilizing natural language processing, voice recognition, and micro-expression analysis [2]. - AI can create dynamic psychological profiles by integrating various data sources, allowing for continuous tracking of students' mental health and tailored interventions [2]. - Process interventions include online counseling platforms and AI chatbots providing 24/7 support, along with immersive training scenarios using VR/AR technologies for emotional regulation and social skills enhancement [3]. - Dynamic assessment and feedback mechanisms enable tracking of student progress and intervention effectiveness, providing valuable insights for mental health educators [3]. - Personalized psychological literacy training can be achieved through AI-driven educational resources, enhancing engagement and adaptability in mental health education [4]. Challenges in AI Empowerment - Ethical and data security concerns arise from the collection of sensitive student data, necessitating strict regulations to protect privacy [6]. - Systemic misjudgments and technical biases can limit the effectiveness of AI interventions, as individual emotional states are complex and subjective [6][7]. - The tendency for generalized AI tools may undermine professional interventions, emphasizing the need for a balance between technology and human interaction in mental health support [7]. Recommendations for Development - Establishing ethical guidelines and data protection standards for AI applications in mental health education is crucial to safeguard student privacy [8]. - Creating a collaborative response mechanism among various departments within universities can enhance the effectiveness of AI-driven mental health services [8]. - Maintaining a humanistic approach in mental health services is essential, ensuring that emotional support and personalized care are not lost in the reliance on technology [9]. - Enhancing the digital literacy of mental health educators is necessary for the effective application of AI tools, promoting a collaborative development of psychological services [9]. Conclusion - The integration of AI in university mental health education presents both opportunities and challenges, requiring a strategic approach that prioritizes ethical considerations and human-centered care while leveraging technological advancements [10].

Core Viewpoint - The article emphasizes the importance of addressing college students' mental health issues as a critical factor affecting the quality of higher education, proposing a comprehensive management system based on big data to enhance prevention, intervention, and recovery strategies [1] Group 1: Prediction Phase - The prediction phase focuses on accurately identifying potential risks by establishing a multi-data collection and dynamic early warning mechanism for college students' mental health education [2] - It involves deep data integration and analysis, capturing students' behaviors and emotional changes through various platforms, including academic performance and social interactions [2] - Environmental factors, such as weather and social contexts, are monitored to assess their impact on students' mental health, particularly during high-stress periods like exams and graduation [2] Group 2: Prevention Phase - The prevention phase advocates for tiered and targeted strategies in mental health education, combining technological empowerment with humanistic care to create a supportive campus environment [4] - Primary prevention includes universal education for at-risk students, utilizing VR technology to simulate stress scenarios and conducting regular mental health assessments [4] - Secondary intervention involves tailored support for low and medium-risk students through online and offline resources, including mental health platforms and professional counseling [4] Group 3: Response Phase - The response phase outlines the need for urgent intervention for high-risk students, ensuring privacy and ethical compliance while coordinating with mental health centers and families [6][7] - A three-tiered treatment approach is proposed, involving emergency interventions, collaborative care among healthcare providers, and personalized follow-up services [7] - Emphasis is placed on protecting student privacy and adhering to ethical standards in mental health interventions [7] Group 4: Recovery Phase - The recovery phase highlights the necessity of a collaborative support network involving schools, families, and healthcare providers to assist students in transitioning from crisis to functional recovery [8] - It suggests creating a multi-dimensional support system that integrates various stakeholders to monitor and address students' mental health needs continuously [8] - The article advocates for a comprehensive support system that spans all stages of student development, from initial enrollment to graduation [9]