Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Zijing Tang

dblp:433/4813 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0004-5931-6453ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 70% Human-AI interaction · 30%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Learning and educational technologies › video-based learning
instructional video
1.012026
Epimopilot : An LLM-infused Epistemic Emotion Support System to Boost Self-regulated Asynchronous Online Video Learning · CHI 2026
Learning and educational technologies
self-regulated learning
1.012026
Epimopilot : An LLM-infused Epistemic Emotion Support System to Boost Self-regulated Asynchronous Online Video Learning · CHI 2026
Learning and educational technologies › online learning
asynchronous learning
0.312026
Epimopilot : An LLM-infused Epistemic Emotion Support System to Boost Self-regulated Asynchronous Online Video Learning · CHI 2026

Methods — techniques the papers use, named apart from their topics

knowledge graph · 1.0emotion recognition · 1.0LLM-powered intervention · 1.0
YearPublicationVenuePosition
2026 Epimopilot : An LLM-infused Epistemic Emotion Support System to Boost Self-regulated Asynchronous Online Video Learning
abstract
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.
Weihang Qin, Yuefan Fang, Zijing Tang, Shimin Kang
CHI4