Anahita Golrang
5 papers · Latest:
Can providing feedback on gaze and mental-effort synchrony improve pair programming performance?
AI feedback on gaze and mental effort synchrony significantly improves pair programming performance, with proactive timing being most effective.
Not All Scaffolds Are Equal: How Initiation Mode Determines EMME Effectiveness in Debugging
This study finds that human-initiated Eye Movement Modeling Examples (EMME) are more effective than automated triggers for novice programmers debugging.
RTMS: A Real-Time Multimodal Scaffolding System for Improving Debugging in Computing Education
A real-time multimodal system (RTMS) uses cognitive load and stress indicators to provide adaptive feedback, significantly improving debugging for students.
Cognitive Alignment Drives Attention: Modeling and Supporting Socially Shared Regulation in Pair Programming
This paper shows cognitive alignment drives attention in pair programming, and AI-driven feedback significantly improves collaborative performance.
ProPACT: A Proactive AI-Driven Adaptive Collaborative Tutor for Pair Programming
ProPACT is a proactive AI tutor that uses multimodal data and XGBoost to predict and improve pair programming collaboration in real-time.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.