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

2026-05-07

AI Summary

This study addresses performance disparities in collaborative programming, which often stem from failures in attentional coordination and cognitive regulation. For the first time, it leverages dyadic joint gaze and synchrony in mental effort as triggers for AI-driven feedback, developing both reactive and predictive multimodal real-time feedback systems that dynamically modulate collaboration through eye-tracking and machine learning models. Experimental results demonstrate that multimodal feedback significantly enhances debugging success rates and task efficiency. Notably, predictive feedback not only further reduces task completion time and increases feedback adoption but also better preserves the learning autonomy of high-performing pairs, thereby establishing a novel paradigm for intelligent support in collaborative programming.

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.

Problem

• pair programming
• joint visual attention
• joint mental effort
• collaborative learning
• AI-supported feedback

Innovation

• joint visual attention
• joint mental effort
• proactive feedback
• AI-supported collaboration
• multimodal feedback