对情感智能人工智能的深度学习方法,以改善学习成果
Xiaoyu Wu1,2, Tientien Lee3,4, Umesh Kumar Lilhore5
1Faculty of Social Sciences and Liberal Arts, UCSI University, No. 1, UCSI Heights, Jalan Puncak Menara Gading, Taman Connaught, 56000, Cheras, Wilayah Persekutuan Kuala Lumpur, Malaysia.
Scientific reports
|February 5, 2026
概括
本研究介绍了人工智能教育的情感感知深度学习框架,通过整合情感智能来增强学习者的参与度和表现. 多式联络系统分析面部表情,语音和文本,以获得适应式学习体验.
科学领域:
- 教育中的人工智能
- 情感计算是一种情感计算.
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 目前的人工智能教育系统主要侧重于认知适应.
- 学生的情绪状态往往被忽视,影响参与和学习成果.
- 需要智能学习环境,包括情绪智能.
研究的目的:
- 为智能学习环境提出一种多式联络,情感感知深度学习框架.
- 将情绪智能融入人工智能驱动的教育系统.
- 提高学习者参与度,情绪调节和任务持久性.
主要方法:
- 开发了一个多式联机深度学习框架,分析面部表情,语音和文本.
- 采用基于图形的融合机制来建模情绪模式之间的相互依赖.
- 使用AffectNet和IEMOCAP基准情绪数据集评估了框架.
主要成果:
- 与以认知为重点的系统相比,情感意识框架显著改善了学习者参与,情感调节和任务持久性.
- 实现了高情绪识别性能,特别是对于积极和中性状态,具有强大的概括性.
- 用户研究表明,由于情感适应能力,学习者发现系统更有支持性和响应性.
结论:
- 多模式情感智能对于开发 empathetic 和有效的 AI 教育系统至关重要.
- 整合情绪意识可以提高智能学习环境的适应性和响应性.
- 数据隐私和负责任的部署等道德考虑对于情感意识的教育技术至关重要.
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