ProPACT用AI提前30秒预测编程搭档协作问题并主动干预，提升调试效率。
ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming
- 基于视觉与认知数据构建双人协作模型，实时追踪注意力与努力程度。
- 提前30秒预测协作失效，干预后调试成功率提升，任务效率提高26%。
- 适合需要实时协同指导的编程教学场景，尤其适合远程配对编程。
有效的结对编程依赖于注意力、认知努力和协同调节的动态协调，但多数自适应学习系统仍以个体为中心且被动响应。本文提出ProPACT，一个主动式AI驱动的协作导师，将协作本身作为教学对象。ProPACT基于联合视觉注意（JVA）、联合认知努力（JME）及个体努力，构建多模态双人学习者模型，并采用基于XGBoost的预测模型，提前最多30秒预测潜在次优协作状态。该预测触发分层自适应策略，在协作高效时逐步减少支持，而在低效时提供最小侵入性支架。对26组结对编程搭档的被试内研究显示，主动反馈显著提升调试成功率、任务效率、反馈采纳率及后续的JVA与JME改善，证明了基于预测的双人自适应在实时协作学习调控中的潜力。
原文摘要 · Abstract (English)
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.
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