用教师共情向量增强AI对特殊教育学生抑郁的评估
Human Empathy as Encoder: AI-Assisted Depression Assessment in Special Education
- 将教师共情转化为9维向量，与学生文本结合进行多模态分析
- 在7级抑郁程度分类中达到82.74%准确率
- 适合关注教育AI伦理与人机协同评估的研究者
在特殊教育等敏感环境中评估学生抑郁极具挑战。标准化问卷难以全面反映学生真实状况，而现有自动化方法在处理学生叙述时，常因缺乏教师基于共情的个性化洞察而表现不佳。为克服这一局限，本文提出人类共情作为编码器（HEAE），一种以人为本的AI框架，实现透明且符合社会伦理的抑郁严重度评估。该方法创新性地融合学生叙述文本与教师生成的9维共情向量（EV），其维度由PHQ-9框架指导，将隐性的共情认知结构化为可输入的AI特征，强化而非替代人类判断。通过优化多模态融合、文本表征与分类架构，实验在7级严重度分类任务中取得82.74%的准确率。本工作为负责任的情感计算提供了新路径，通过结构化嵌入人类共情实现更合乎伦理的智能评估。
原文摘要 · Abstract (English)
Assessing student depression in sensitive environments like special education is challenging. Standardized questionnaires may not fully reflect students' true situations. Furthermore, automated methods often falter with rich student narratives, lacking the crucial, individualized insights stemming from teachers' empathetic connections with students. Existing methods often fail to address this ambiguity or effectively integrate educator understanding. To address these limitations by fostering a synergistic human-AI collaboration, this paper introduces Human Empathy as Encoder (HEAE), a novel, human-centered AI framework for transparent and socially responsible depression severity assessment. Our approach uniquely integrates student narrative text with a teacher-derived, 9-dimensional "Empathy Vector" (EV), its dimensions guided by the PHQ-9 framework,to explicitly translate tacit empathetic insight into a structured AI input enhancing rather than replacing human judgment. Rigorous experiments optimized the multimodal fusion, text representation, and classification architecture, achieving 82.74% accuracy for 7-level severity classification. This work demonstrates a path toward more responsible and ethical affective computing by structurally embedding human empathy
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