DAPWeb: Construct-Aligned Evaluation of MLLMs for Web-Based Child Mental Screening

Child mental health screening faces growing challenges from rising psychological problems and limited professional access. Traditional self-report tools rely on verbal ability and self-awareness, limiting their validity in younger children. Projective drawing tests offer a nonverbal alternative, with the Draw-A-Person (DAP) test widely used to elicit psychological cues from drawings. While translating DAP test into Web-based screening, multimodal large language models (MLLMs) are emerging as a key enabling mechanism. However, their ability to deliver construct-level clarity and interpretive consistency for responsible deployment has not been systematically evaluated. To address this gap, we propose DAPWeb, a construct-aligned evaluation framework that assesses whether MLLMs can reliably support DAP-based child mental screening in Web environments. DAPWeb introduces a clinically grounded benchmark derived from real drawings and defines task-structured evaluation across six psychological constructs and three essential screening abilities: determination, detection, and comparison. The metrics emphasize construct validity and cross-drawing consistency, reflecting real-world early-screening workflows. Experiments across MLLMs reveal substantial gaps from human experts in most abilities. Yet, the comparable performance on determination suggests that DAP test can serve as a feasible component of Web-based early screening under structured interpretation. Thus, DAPWeb provides a replicable paradigm for responsible Web AI in child mental health.