Modeling users' cognitive states (e.g., cognitive load and decision confidence) is essential for building adaptive AI in high-stakes decision-making. While eye tracking provides non-invasive behavioral signals correlated with cognitive effort, prior work has not systematically examined how AI assistance contexts, specifically varying advice reliability and user heterogeneity, can alter the mapping between gaze signals and cognitive states. We conducted a within-subject lab eye-tracking study (N=54) on factual verification tasks under three conditions: No-AI, Correct-AI advice, and Incorrect-AI advice. We analyze condition-dependent changes in self-reports and eye-tracking patterns and evaluate the robustness of eye-tracking-based user modeling. Results show that AI advice increases decision confidence compared to No-AI, while Correct-AI is associated with lower perceived cognitive load and more efficient gaze behavior. Crucially, predictive modeling is context-sensitive: the relationship between eye-tracking signals and cognitive states shifts across AI conditions. Finally, fusing eye-tracking features with user priors (demographics, AI literacy/experience, and propensity to trust technology) improves cross-participant generalization. These findings support condition-aware and personalized user modeling for cognitively aligned adaptive AI systems.

翻译：建模用户的认知状态（例如认知负荷和决策信心）对于在高风险决策中构建自适应AI至关重要。尽管眼动追踪能够提供与认知努力相关的非侵入性行为信号，但先前研究尚未系统性地探讨AI辅助情境（特别是建议可靠性变化与用户异质性）如何改变凝视信号与认知状态之间的映射关系。我们开展了一项受试者内实验室眼动追踪研究（N=54），在无AI、正确AI建议和错误AI建议三种条件下执行事实核查任务。我们分析了条件依赖的自述报告与眼动模式变化，并评估了基于眼动追踪的用户建模鲁棒性。结果表明，与无AI条件相比，AI建议增加了决策信心，而正确AI建议与更低的主观认知负荷及更高效的凝视行为相关。关键的是，预测建模具有情境敏感性：眼动追踪信号与认知状态之间的关系随AI条件而变化。最后，将眼动追踪特征与用户先验（人口统计学信息、AI素养/经验及技术信任倾向）相融合，提升了跨被试的泛化能力。这些发现支持为认知对齐的自适应AI系统构建条件感知且个性化的用户建模。