解码学生心智：利用对话代理进行心理和学习分析
Decoding Student Minds: Leveraging Conversational Agents for Psychological and Learning Analysis
AI总结：
本文提出一种结合多模态数据和语义推理的对话代理，用于实时分析学生认知和情感状态，提升学习表现和情感福祉。
摘要：
本文提出了一种心理意识的对话代理，旨在增强教育环境中学习表现和情感福祉。该系统结合了大型语言模型（LLMs）、知识图谱增强的BERT（KG-BERT）以及双向长短期记忆（LSTM）与注意力机制，以实时分类学生的认知和情感状态。不同于以往仅限于辅导或情感支持的聊天机器人，我们的方法利用多模态数据——包括文本语义、语调语音特征和时间行为趋势——来推断参与度、压力和概念理解。一项针对大学生的试点研究表明，与基线方法相比，该方法在提高动机、减少压力和实现适度学业进步方面表现出色。这些结果凸显了将语义推理、多模态融合和时间建模整合起来，以支持自适应、以学生为中心的教育干预的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
