用机器人+视觉语言模型评估儿童心理状态，效果有潜力但需警惕性别偏差。
Robot-Led Vision Language Model Wellbeing Assessment of Children
- 机器人引导孩子看图讲故事，模型按心理测评标准分析语言内容。
- 模型对无问题儿童判断较准，但对有临床问题的识别率仍偏低。
- 女孩误判率更高，提示模型可能受性别特征影响，需谨慎使用。
本研究提出一种由机器人主导的儿童心理健康评估新方法，基于视觉语言模型（VLM）。受儿童投射测验（CAT）启发，社交机器人NAO向儿童展示图片，引导其描述图像内容，随后由VLM依据CAT标准进行评估，并与专业心理学家的判断进行对比。结果表明，尽管VLM在识别无心理困扰的儿童时表现出中等可靠性，但在准确识别存在临床关注点的案例方面能力有限。此外，尽管模型在不同年龄和性别群体中的表现总体一致，但对女孩的假阳性率显著偏高，表明模型可能对性别属性敏感。该研究揭示了将VLM融入机器人主导的心理评估中的潜力与挑战。
原文摘要 · Abstract (English)
This study presents a novel robot-led approach to assessing children's mental wellbeing using a Vision Language Model (VLM). Inspired by the Child Apperception Test (CAT), the social robot NAO presented children with pictorial stimuli to elicit their verbal narratives of the images, which were then evaluated by a VLM in accordance with CAT assessment guidelines. The VLM's assessments were systematically compared to those provided by a trained psychologist. The results reveal that while the VLM demonstrates moderate reliability in identifying cases with no wellbeing concerns, its ability to accurately classify assessments with clinical concern remains limited. Moreover, although the model's performance was generally consistent when prompted with varying demographic factors such as age and gender, a significantly higher false positive rate was observed for girls, indicating potential sensitivity to gender attribute. These findings highlight both the promise and the challenges of integrating VLMs into robot-led assessments of children's wellbeing.
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