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.

翻译：困难溢出效应与次优求助行为对数字化任务的序列化、知识密集型特性构成了挑战。在线调查中，难题会消耗心理能量并损害后续问题的表现，而用户往往未能意识到需要协助，或因满足现状而缺乏寻求帮助的动力。我们开发了一种基于皮肤电活动与鼠标移动的主动自适应系统，用于预测受访者何时需要支持。通过结合基于规则的阈值自适应机制，个性化分类器能够触发及时的大语言模型澄清与解释。在一项被试内设计研究（N=32）中，对齐自适应时机与错位自适应及随机自适应控制条件进行了比较。对齐自适应辅助使回答准确率提升21%，将假阴性率从50.9%降至22.9%，并改善了感知效率、可靠性与善意度。适时干预能够防止响应质量下降的连锁反应，表明使支持与认知状态对齐可同时改善结果与用户体验。这为基于调查的研究提供了更有效、个性化的LLM辅助支持。