用对话模型实时分析学生心理与学习状态，提升专注度和情绪健康。
Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis
- 融合文本、语音和行为数据，用多模态模型判断学生情绪与理解程度。
- 45名大学生参与实验，动机提升，压力下降，学业表现中等改善。
- 首次系统拆解知识图谱与各模态贡献，适合教育科技与认知计算研究者。
本文提出一种具备心理感知能力的对话式教育助手，旨在提升学习成效与情感健康。系统结合大语言模型（LLMs）、知识图谱增强的BERT（KG-BERT）以及带注意力机制的双向LSTM网络，实时分类学生的认知与情感状态。不同于以往仅提供教学或情绪支持的聊天机器人，本方法利用多模态数据——包括文本语义、语音韵律特征与时间行为趋势——来推断学习投入度、压力水平与概念理解。一项针对45名大学生的初步研究显示，相比单一模态基线，该系统显著提升学习动机，降低压力，并带来中等程度的学术进步。研究明确指出样本量较小的探索性质，报告了效应量与评分者一致性，还进行了消融分析，分别评估知识图谱组件及各模态的贡献。结果表明，语义推理、多模态融合与时序建模相结合，在实现自适应、以学生为中心的干预方面具有潜力，但也存在规模与模态平衡的当前局限。
原文摘要 · Abstract (English)
This paper presents a psychologically-aware conversational agent designed to enhance both learning performance and emotional well-being in educational settings. The system combines Large Language Models (LLMs), a knowledge graph-enhanced BERT (KG-BERT), and a bidirectional Long Short-Term Memory (LSTM) network with attention to classify students' cognitive and affective states in real time. Unlike prior chatbots limited to either tutoring or affective support, our approach leverages multimodal data-including textual semantics, prosodic speech features, and temporal behavioral trends-to infer engagement, stress, and conceptual understanding. A pilot study with 45 university students demonstrated improved motivation, reduced stress, and moderate academic gains compared to unimodal baselines. We explicitly discuss the exploratory nature of this small-sample pilot, report effect sizes and inter-rater reliability alongside significance tests, and provide an ablation analysis isolating the contribution of the knowledge-graph component and of each modality. These results underline the promise-while acknowledging the current limits in scale and modality balance-of integrating semantic reasoning, multimodal fusion, and temporal modeling to support adaptive, student-centered educational interventions.
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