发表机构
The Hong Kong Polytechnic University; University of California, San Diego（香港理工大学; 加州大学圣地亚哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TutorLoop通过传感器在环和深度强化学习优化反馈，在N=187研究中以更少干预提升注意力、降低负担并增强学习效果。
AI 中文摘要
我们提出了TutorLoop，一种传感器在环系统，通过基于实时认知状态的自适应反馈来调节学生学习行为。与先前直接依赖场景特定内容的大型语言模型（LLM）导师不同，TutorLoop基于通过网络摄像头捕获的传感器信号运作。此外，与目光短浅的直接认知到反馈映射不同，该系统采用深度强化学习（DRL）代理来优化整个学习过程中的反馈类型。最后，另一个LLM导师将反馈精炼为类人的、上下文感知的消息。我们在一个大规模用户研究（N=187）中评估了TutorLoop，其中离线训练的模型直接应用于新的学习任务而无需重新训练。结果表明，TutorLoop提供了更少但更有效的干预，提高了注意力，减少了工作负担，增加了参与度，并最终提升了学习成果。这些发现凸显了闭环、传感器驱动的反馈在支持学习的可扩展人机集成系统中的潜力。
英文摘要
We present TutorLoop, a sensor-in-the-loop system that regulates student learning behaviors by delivering adaptive feedback based on real-time cognitive states. Unlike prior large language model (LLM) tutors that directly depend on scenario-specific content, TutorLoop operates on sensor-derived signals captured via webcams. Moreover, unlike direct cognitive-to-feedback mappings that are short-sighted, the system employs a deep reinforcement learning (DRL) agent to optimize the feedback type across the entire learning process. Finally, another LLM tutor refines feedback into human-like, context-aware messages. We evaluate TutorLoop in a large-scale user study (N=187), where a model trained offline is directly applied to a new learning task without retraining. Results show that TutorLoop provides less frequent yet more effective interventions, improving attention, reducing workload, increasing engagement, and ultimately enhancing learning outcomes. These findings highlight the potential of closed-loop, sensor-driven feedback for scalable human-AI integrated systems to support learning.
