ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming
ProPACT shifts adaptive learning from individual-centric to collaboration-centric by modeling joint attention and cognitive load across paired learners. Using XGBoost forecasting, the system predicts suboptimal team dynamics 30 seconds ahead and delivers context-aware scaffolding that withdraws as coordination improves. This work signals a maturing frontier in multimodal learner modeling and proactive intervention, moving beyond reactive tutoring toward systems that treat interpersonal coordination as a learnable skill. The approach has implications for team-based knowledge work and human-AI collaboration design.
Modelwire context
ExplainerProPACT's core novelty isn't just modeling pairs instead of individuals, but treating the prediction of coordination breakdown as a forecasting problem solvable 30 seconds in advance. That temporal buffer is what enables proactive scaffolding rather than reactive correction, fundamentally changing when and how the system intervenes.
This work sits at the intersection of two threads in recent coverage. It shares DNA with the Adaptive Querying paper (arXiv cs.CL, May 1st) in using learned models to personalize intervention timing and content, but scales that logic from individual preference elicitation to real-time team dynamics. More directly, it echoes the memory-learning framework from MemCoE (May 1st), which also treats a coordination problem (what to retain across context) as a learnable optimization task rather than a static rule. Where those papers focus on individual cognition or LLM internals, ProPACT extends the pattern to interpersonal coordination, suggesting a broader shift toward systems that learn when and how to intervene rather than applying fixed heuristics.
If ProPACT's 30-second forecasting accuracy holds above 75% on held-out pair sessions from different domains (not just the training domain), that validates the claim that joint attention patterns are predictive across contexts. If it doesn't generalize, the contribution narrows to domain-specific tutoring rather than a reusable coordination model.
Coverage we drew on
- Adaptive Querying with AI Persona Priors · arXiv cs.CL
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MentionsProPACT · XGBoost · Joint Visual Attention · Joint Mental Effort
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