感知调查所遗漏的：通过用户建模理解并个性化主动LLM支持
Sensing What Surveys Miss: Understanding and Personalizing Proactive LLM Support by User Modeling
AI总结：
通过用户建模实现主动LLM支持，提升在线调查的响应准确性和用户满意度。
摘要：
困难的溢出和次优的帮助寻求挑战了数字任务的连续性和知识密集型性质。在在线调查中，难题问题会消耗心理能量并影响后续问题的表现，而用户往往无法识别何时需要帮助或可能满足，缺乏寻求帮助的动力。我们开发了一个主动、适应性的系统，利用电导活动和鼠标移动来预测受访者需要支持的时间。具有规则阈值适应的个性化分类器触发及时的LLM基于澄清和解释。在一项被试内研究（N=32）中，对齐适应性与错位适应性和随机适应性进行了比较。对齐适应性帮助提高了响应准确率21%，将误报率从50.9%降低到22.9%，并提高了感知效率、可靠性和仁慈度。适当的时间干预可以防止响应质量的连锁下降，显示了与认知状态对齐支持可以改善结果和用户体验。这使调查研究中的LLM辅助支持更加有效和个性化。
英文摘要：
Difficulty spillover and suboptimal help-seeking challenge the sequential, knowledge-intensive nature of digital tasks. In online surveys, tough questions can drain mental energy and hurt performance on later questions, while users often fail to recognize when they need assistance or may satisfy, lacking motivation to seek help. We developed a proactive, adaptive system using electrodermal activity and mouse movement to predict when respondents need support. Personalized classifiers with a rule-based threshold adaptation trigger timely LLM-based clarifications and explanations. In a within-subjects study (N=32), aligned-adaptive timing was compared to misaligned-adaptive and random-adaptive controls. Aligned-adaptive assistance improved response accuracy by 21%, reduced false negative rates from 50.9% to 22.9%, and improved perceived efficiency, dependability, and benevolence. Properly timed interventions prevent cascades of degraded responses, showing that aligning support with cognitive states improves both the outcomes and the user experience. This enables more effective, personalized LLM-assisted support in survey-based research.
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。
