设计面向在线学习的情感自适应聊天机器人：用户控制、自动化与透明度之间的权衡

中文摘要 AI 辅助

在线学生经常面临沮丧、孤立和动机波动等情感挑战，这些挑战可能阻碍他们在在线学习中的持续参与。虽然情感聊天机器人在医疗保健和健康领域已显示出潜力，但大多数学习支持聊天机器人主要侧重于认知辅助，对学习者的情感和动机体验关注有限。在本研究中，我们通过迭代设计过程探讨如何为在线学习设计自适应情感聊天机器人支持。通过一项混合方法的需找研究（n=38），我们识别出学习者对情感支持的反应存在显著差异，没有单一策略被普遍偏好。这促使我们开发了三种自适应设计替代方案：用户控制型、AI驱动型和混合共同控制型自适应，并考察了自动化、透明度和用户控制之间的关键权衡。最终评估（n=25）显示，88%的参与者偏好自适应混合设计而非非自适应替代方案，凸显了平衡透明度、用户控制和交互简单性的价值。我们讨论了在情感挑战性的在线学习环境中自适应情感聊天机器人系统的设计启示。

英文摘要

Online students often face emotional challenges such as frustration, isolation, and fluctuating motivation, which can hinder sustained engagement in online learning. While affective chatbots have shown promise in healthcare and wellness, most learning support chatbots focus primarily on cognitive assistance with limited attention to learners' emotional and motivational experiences. In this work, we investigate how adaptive affective chatbot support can be designed for online learning through an iterative design process. Through a mixed-methods needfinding study (n=38), we identified substantial variation in how learners respond to affective support, with no single strategy universally preferred. This led us to develop three adaptive design alternatives: user-controlled, AI-driven, and hybrid co-controlled adaptation, and examine key trade-offs between automation, transparency, and user control. A final evaluation (n=25) showed that 88% of participants favored the adaptive hybrid design over a non-adaptive alternative, highlighting the value of balancing transparency, user control, and interaction simplicity. We discuss design implications for adaptive affective chatbot systems in emotionally challenging online learning contexts.

发表机构

• Georgia Institute of Technology（佐治亚理工学院）

机构由 AI 辅助整理，请以论文原文为准。