ProPACT (Proactive AI-Driven Adaptive Collaborative Tutor) is an AI-driven adaptive tutoring system for pair programming that treats collaboration itself as the object of instruction. Unlike individual-centric, reactive systems, it models dyadic learning states in real time and intervenes before collaborative breakdowns occur, using multimodal sensing and predictive forecasting.
Authors: Anahita Golrang, Kshitij Sharma, Simon Dehaen, Olga Viberg · arXiv:2605.02703 · Within-subjects experiment with 26 pair-programming dyads (52 CS/Engineering students)
Key Findings
1. Significant performance gains from proactive feedback. Dyads receiving ProPACT feedback achieved substantially higher debugging success (t[49.96] = −13.51, p < .0001) and completed tasks more efficiently (t[44.70] = 4.39, p < .0001) compared to the no-feedback control condition.
2. Dyadic sensing enables predictive intervention. ProPACT constructs a multimodal dyadic learner model from Joint Visual Attention (JVA — cosine similarity of gaze distributions over 30-second windows), Joint Mental Effort (JME — cross-recurrence quantification of pupil-diameter signals), and individual Mental Effort (IPA from pupillary fluctuations). An XGBoost-based forecaster predicts sub-optimal collaboration states up to 30 seconds in advance.
3. Five-tier adaptive feedback hierarchy works. The system escalates through minimally intrusive scaffolds: (A1) do nothing when collaboration is productive; (A2) temporarily enable GitHub Copilot when cognitive strain rises; (A3) show a gaze-awareness tool highlighting the partner's visual focus; (A4) issue unobtrusive dialogue prompts to re-align mental effort; and (A5) provide directive task-based hints only as a last resort. Signals are discretized against a normalized resting baseline using a ±2SD criterion (High / Average / Low).
4. Post-intervention gains in collaborative regulation. Beyond task-level improvements, dyads showed sustained increases in JVA and JME after the intervention, indicating that the system fostered durable collaborative skills rather than just providing momentary assistance.
Implications
ProPACT represents a shift from individual to dyadic learner modeling in intelligent-tutoring-systems. By treating the pair — not the person — as the unit of analysis, it addresses a long-standing gap in collaborative-learning support. Traditional ITS architectures focus on individual cognition; ProPACT demonstrates that multimodal signals (gaze, pupil dilation) can be fused to model the health of a collaborative process in real time.
The proactive forecasting approach is a departure from reactive feedback paradigms common in adaptive-learning-systems. By predicting breakdowns 30 seconds ahead, ProPACT avoids the latency inherent in "detect-then-respond" architectures, allowing scaffolds to arrive before students experience frustration or disengagement. This has implications for engagement-metrics and real-time classroom orchestration.
For cs-education specifically, ProPACT validates that AI-assisted pair programming can improve both task outcomes and collaborative skill development. The system's integration with collaborative-ai-tutoring workflows suggests a future where AI tutors monitor not just what students produce (code), but how they work together.
The gaze-awareness tool (A3) is a particularly novel intervention: rather than providing didactic content, it surfaces the partner's attentional focus as a lightweight nudge toward shared attention. This aligns with multimodal-ai-tutoring research emphasizing non-verbal channels for learning support.
Related Pages
- collaborative-learning — Foundational paradigm for dyadic and group learning processes
- collaborative-ai-tutoring — AI systems that support multi-learner interaction
- intelligent-tutoring-systems — Broader class of adaptive instructional systems
- adaptive-learning-systems — Systems that adjust to learner state in real time
- cs-education — Context domain for pair programming interventions
- engagement-metrics — Measurement frameworks for attentional and cognitive engagement
- multimodal-ai-tutoring — AI tutors that fuse multiple sensor channels