Epimopilot : An LLM-infused Epistemic Emotion Support System to Boost Self-regulated Asynchronous Online Video Learning

Self-regulated learning through asynchronous online videos lacks direct social-emotional interactions, often resulting in isolated learning experiences. We present Epimopilot, a system transforming emotional cues from time-synchronized comments (danmaku) into personified virtual companions to create a sense of community. Epimopilot visualizes collective epistemic emotions through emotional companions while providing LLM-powered interventions upon detecting learning difficulties through emotional cues. A within-subjects experiment (N=32) comparing Epimopilot with a baseline system demonstrated significant improvements in learning persistence, engagement, and knowledge comprehension. Results revealed that emotion-infused companion interactions combined with targeted AI assistance significantly enhanced self-regulated learning experiences. The analysis yielded design principles for recognizing epistemic emotions and integrating collective emotional cues with a knowledge graph. This work contributes an innovative approach to foster a co-presence learning experience in asynchronous environments through emotion recognition and adaptive cognitive support.