Analysis of Mental Health Assessment of Junior High School Students Based on AIGC Human-Computer Interaction Model

Adolescent mental health challenges are worsening. Traditional paper- or web-based screening tools lack generalizability, timeliness, and objectivity—and are especially vulnerable to social desirability bias and stigma, causing students to conceal distress. AI-generated content (AIGC) offers a promising alternative for mental health assessment. Yet, the validity and acceptability of AIGC-based conversational assessments in real school settings remain untested. This study involved 584 seventh- and eighth-grade students from Chunjiang Middle School (Hangzhou, Zhejiang) to: (1) assess agreement between the AIGC conversational model and the school's existing observation list; (2) test its ability to identify at-risk students missed by routine observation; and (3) evaluate student acceptance. Results show the AIGC model aligns well with current practices, detects overlooked cases, and is well accepted—making it a viable supplement to traditional screening and teacher observation. It supports a shift from passive intervention to active prevention in school mental health. This study provides empirical evidence and practical guidance for scaling AIGC-based screening in K–12 settings and advances efforts to build a destigmatized adolescent mental health service system.