Effective pair programming depends on coordination of attention, cognitive effort, and joint regulation over time, yet most adaptive learning systems remain individual-centric and reactive. This paper introduces ProPACT, a proactive AI-driven adaptive collaborative tutor that treats collaboration itself as the object of instruction. ProPACT constructs a multimodal dyadic learner model based on Joint Visual Attention (JVA), Joint Mental Effort (JME), and individual mental effort, and employs an XGBoost-based forecasting model to predict emerging suboptimal collaboration states up to 30 seconds in advance. These predictions drive a hierarchical adaptive policy that delivers minimally intrusive scaffolds while fading support during productive collaboration. A within-subject study with 26 pair-programming dyads shows that proactive feedback significantly improves debugging success, task efficiency, feedback uptake, and post-intervention gains in JVA and JME, demonstrating the potential of forecast-driven dyadic adaptivity for real-time collaborative learning regulation.

翻译：有效的配对编程依赖于注意力协调、认知努力以及随时间变化的联合调节，然而大多数自适应学习系统仍以个体为中心且反应被动。本文介绍ProPACT，一种将协作本身作为教学对象的、基于AI的前瞻性自适应协作导师。ProPACT基于联合视觉注意力（JVA）、联合心理努力（JME）以及个体心理努力构建多模态二元学习者模型，并采用基于XGBoost的预测模型，提前最多30秒预测即将出现的次优协作状态。这些预测驱动分层自适应策略，在协作高效时提供最小干扰的支架支持并逐步撤销。一项针对26组配对编程搭档的受试者内研究表明，前瞻性反馈显著提高了调试成功率、任务效率、反馈采纳度以及干预后JVA和JME的提升，证明了基于预测的二元自适应性在实时协作学习调节中的潜力。