Collaborative AI Tutoring

ProPACT constructs a real-time model of pair collaboration using three signals:

Most adaptive learning systems are individual-centric and reactive. ProPACT treats collaboration itself as the object of instruction, intervening before dyadic breakdowns occur by forecasting suboptimal states up to 30 seconds in advance.^Golrang Propact Pair Programming 2026

The Dyadic Learner Model

ProPACT constructs a real-time model of pair collaboration using three signals:

Signal	Description	Measurement
JVA (Joint Visual Attention)	Shared attentional focus	Cosine similarity of gaze distributions over 30-second windows
ME (Mental Effort)	Individual cognitive load	Index of Pupillary Activity (IPA) over 10-second windows
JME (Joint Mental Effort)	Cognitive engagement synchrony	Cross-recurrence of synchronized ME signals

All signals are discretized against a resting baseline using ±2SD bins (High, Average, Low).^Golrang Propact Pair Programming 2026

Proactive Forecasting Engine

Model: XGBoost predicts JVA, JME, and ME over a 30-second horizon

Policy: Rule-based pedagogical hierarchy prioritizes minimally intrusive support

Principle: Fade support when collaboration is productive; escalate only when breakdown risk increases

Adaptive Feedback Hierarchy

Intervention	Trigger	Intrusiveness
A1: Do nothing	MEs=AVG, JVA=H, JME=H	None (desired state)
A2: GitHub Copilot	MEs=HH or LL, or (MEs=HL and JVA=L)	Low (autocomplete)
A3: Gaze-awareness tool	JVA=Low	Low (visual cue highlighting partner's gaze)
A4: Dialogue prompt	JME=Low	Medium (editor prompt encouraging brief dialogue)
A5: Task-based hint	Both MEs=High (last resort)	High (directive hint)

The hierarchy embodies the principle that proactive, minimally intrusive scaffolds outperform reactive, directive interventions.^Golrang Propact Pair Programming 2026

Key Results

In a within-subjects study with 26 pair-programming dyads (52 CS/Engineering students), ProPACT feedback vs. control showed:

Measure	Effect
Debugging success	Significantly higher (t(49.96) = −13.51, p < .0001)
Debugging time	Significantly lower / more efficient (t(44.70) = 4.39, p < .0001)
Feedback uptake	Significantly higher (F(49.81) = −17.69, p < .0001)

Post-intervention gains in JVA and JME suggest improved collaborative regulation beyond immediate task completion.^Golrang Propact Pair Programming 2026

Relationship to Tutoring-Specific Design

ProPACT exemplifies tutoring-specific design: it does not give answers but structures collaboration through graduated scaffolds (mimicking a skilled pair-programming mentor). Like the best tutoring systems, it targets the zone of proximal development at the dyadic level—ensuring partners are neither overloaded nor under-challenged. It also preserves Metacognition by requiring learners to actively monitor and regulate their collaboration.^Golrang Propact Pair Programming 2026

Implications

For CS education: Real-time dyadic regulation is tractable and effective; eye tracking + ML forecasting is viable at classroom scale

For adaptive systems: The "tutoring" unit need not be an individual—collaboration itself can be scaffolded

For transfer: Does improved dyadic regulation transfer to unsupervised pair work? Not yet tested

Connected Concepts

Zone Of Proximal Development

Metacognition

Connected Articles

Golrang Propact Pair Programming 2026

Tutoring Specific Vs General AI

Citation

Golrang, A., Sharma, K., Dehaen, S., & Viberg, O. (2026). ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming. arXiv:2605.02703.