Research on the construction of an adaptive mindfulness-based cognitive therapy system based on large language models and its efficacy in addressing college students' anxiety

To handle the problem of resource scarcity and lack of personalization and adaptability in the traditional approach to mindfulness-based cognitive therapy (MBCT) in dealing with college students with anxiety disorders, this study proposes the use of the natural language interface and adaptability properties of large language models (LLMs) to come up with an intelligent college-level MBCT intervention system. To begin with, the system functional requirements and the logic of the system intervention were explained in line with the fundamental ideas of MBCT and the cognitive features of students with anxiety. Second, it was created through a four-stage adaptive architecture that includes perception analysis, intervention, and feedback, providing a dynamic process for user status identification, personal generation of intervention plans, and real-time optimization of the intervention process. Finally, there was a randomized controlled trial whose subjects were 120 college students with anxiety who were separated into an experimental group and a control group. The effectiveness of the intervention was assessed using tools such as the Self-Rating Anxiety Scale (SAS), the Mindful Attention Awareness Scale (MAAS), and the Automatic Thoughts Questionnaire (ATQ). The findings indicated that the SAS score of the experimental group (38.26 ± 5.12) was significantly less than that of the control group (45.89 ± 6.34), the MAAS score (68.73 ± 7.25) was significantly higher than that of the control group (56.41 ± 8.13); and the ATQ score (42.15 ± 6.08) was significantly less than that of the control group. The study reveals that an adaptive MBCT system using large language models may be effective in reducing anxiety symptoms among college students, improving mindfulness, and enhancing cognitive processes, offering a new, readily available strategy for addressing anxiety in college students.