Human Empathy as Encoder: AI-Assisted Depression Assessment in Special Education

TL;DR

This paper presents HEAE, a human-centered AI framework that combines student narratives with teacher-derived empathy vectors to improve depression severity assessment in special education, emphasizing transparency and ethical considerations.

Contribution

It introduces a novel integration of human empathy into AI models for depression assessment, enhancing accuracy and interpretability in sensitive educational settings.

Findings

Achieved 82.74% accuracy in 7-level severity classification.

Effectively integrated empathy vectors with narrative text for improved assessment.

Demonstrated a path toward ethical affective computing with human-AI collaboration.

Abstract

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