<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Under the current background of increasingly fierce social competition, the psychological pressure of college student groups is increasing, and mental health problems are becoming more and more significant. Based on the cognitive model of mental health and the theory of cognitive-behavioral psychotherapy, this paper constructs a comprehensive mental health education mechanism covering school, family and society. A mental health abnormality detection device was developed in the study, which integrates a mental health detection model and an early warning mechanism, aiming to detect students’ mental health problems in time. Empirical Analysis shows that the recognition accuracy of the mental health abnormality detection model constructed in this study is as high as 0.8748. Regarding the assessment of teaching effectiveness, the pre- and posttest changes in the mental health scores of the control group and the experimental group are significant, with the test value of the difference between the control group being 1.242, while that of the experimental group is 6.480. The average posttest score of the experimental group is 6.15, which is a decrease of 11.75 compared to that of the control group. This result indicates that the mental health education model proposed in this paper can significantly enhance and effectively improve students’ mental health. This study provides an effective teaching method for college students’ mental health education, which can help guide the practice of mental health education in schools.</p>
</abstract>

Under the current background of increasingly fierce social competition, the psychological pressure of college student groups is increasing, and mental health problems are becoming more and more significant. Based on the cognitive model of mental health and the theory of cognitive-behavioral psychotherapy, this paper constructs a comprehensive mental health education mechanism covering school, family and society. A mental health abnormality detection device was developed in the study, which integrates a mental health detection model and an early warning mechanism, aiming to detect students’ mental health problems in time. Empirical Analysis shows that the recognition accuracy of the mental health abnormality detection model constructed in this study is as high as 0.8748. Regarding the assessment of teaching effectiveness, the pre- and posttest changes in the mental health scores of the control group and the experimental group are significant, with the test value of the difference between the control group being 1.242, while that of the experimental group is 6.480. The average posttest score of the experimental group is 6.15, which is a decrease of 11.75 compared to that of the control group. This result indicates that the mental health education model proposed in this paper can significantly enhance and effectively improve students’ mental health. This study provides an effective teaching method for college students’ mental health education, which can help guide the practice of mental health education in schools.

Exploring the teaching mode of college students’ psychological education under artificial intelligence technology guided by cognitive-behavioral theory

Party Committee Organization Department of Jiangsu University of Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Under the current background of increasingly fierce social competition, the psychological pressure of college student groups is increasing, and mental health...

Exploring the teaching mode of college students’ psychological education under artificial intelligence technology guided by cognitive-behavioral theory

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 24 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0597

Palabras clave
Cognitive model, Cognitive behavior, Abnormality detection model, Mental health early warning, Mental health teaching

© 2024 Zhixian Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the teaching mode of college students’ psychological education under artificial intelligence technology guided by cognitive-behavioral theory

Publicado en línea: 26 feb 2024

Páginas: -

Recibido: 15 ene 2024

Aceptado: 24 ene 2024

DOI: https://doi.org/10.2478/amns-2024-0597

Palabras claveCognitive model, Cognitive behavior, Abnormality detection model, Mental health early warning, Mental health teaching

© 2024 Zhixian Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Cognitive model, Cognitive behavior, Abnormality detection model, Mental health early warning, Mental health teaching