In mainland China, the mental health of vocational high school students has been increasingly recognized as a critical issue by both the government and scholars. Positive education, a branch of positive psychology, has been proven to significantly enhance adolescents’ well-being and academic performance. Exploring how to effectively integrate positive education theories or models with traditional teaching methods in vocational classrooms is a promising research direction. To address this, the present study uses the Mechanical Drawing course as a case study, combining the PERMA model with the traditional PBL teaching model to propose an innovative and practical teaching method: the PERMA-based PBL teaching model. The study involved 207 first-year vocational high school students from four parallel classes. Based on their pre-test scores, the students were divided into two parallel groups (A and B) for a crossover experimental design. Quantitative data were collected using the Inventory of Positive Mental Characters among Chinese Middle School Students and a course survey questionnaire. Each group underwent pre-test, mid-test, and post-test assessments, with quantitative analysis conducted via t-tests to evaluate the advantages of the new teaching model compared to traditional teaching methods. The results indicated that the new teaching model significantly outperformed the traditional teaching model in enhancing all dimensions of vocational high school students’ positive psychological qualities (p < 0.001). Additionally, the new teaching model was superior in improving students’ Learning Interest (p < 0.001), Learning Motivation (p < 0.001), and Learning Attitude (p < 0.05). Furthermore, vocational students expressed a stronger preference for the PERMA-based PBL teaching model compared to the traditional teaching model (p < 0.001). This study explores a practical pathway for integrating positive psychology theories into classroom settings and provides a reference for future research in this field. © © 2025 Lu, Guo and Wang.
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