Can providing feedback on gaze and mental-effort synchrony improve pair programming performance?

TL;DR

This study explores how AI-supported feedback based on gaze and mental effort synchrony can enhance pair programming by improving collaboration, debugging efficiency, and learner agency through reactive and proactive interventions.

Contribution

It introduces novel AI-driven feedback mechanisms grounded in joint visual attention and mental effort, demonstrating their effectiveness in real-time collaborative programming tasks.

Findings

Multimodal feedback improves debugging success and efficiency.

Reactive feedback combined with gaze and effort cues yields strong performance gains.

Proactive feedback reduces time on task and increases constructive feedback uptake.

Abstract

Pair programming is a widely used collaborative learning practice in computer science education yet its effectiveness varies substantially due to breakdowns in coordination attention and cognitive regulation between partners. This paper investigates whether AI supported feedback grounded in joint visual attention and joint mental effort can improve collaborative programming performance and how feedback timing shapes learner AI interaction. Two experimental studies using dual eye tracking capture real time indicators of collaborative regulation during debugging tasks. Study 1 examines reactive feedback that intervenes when observed joint visual attention or joint mental effort deviates beyond predefined thresholds while Study 2 evaluates proactive feedback that forecasts future regulatory breakdowns using machine learning models and intervenes pre emptively. Across both studies feedback effectiveness is assessed through debugging success time on task and feedback uptake reflected in code changes. Multimodal feedback significantly improves collaborative performance compared to no feedback conditions. Reactive feedback yields strong gains in debugging success and efficiency particularly when joint visual attention and joint mental effort based feedback are combined. Proactive forecast based feedback further enhances performance reduces time on task and increases constructive feedback uptake while relying less on intrusive interventions. Proactive feedback better preserves learner agency by maintaining optimal collaboration states, particularly for high-performing pairs. These findings demonstrate that gaze and mental effort synchrony can serve as reliable actionable triggers for AI supported collaborative learning highlighting the importance of feedback timing transparency and anticipatory regulation in supporting effective pair programming.