Demo Data from VLDB 2026 / reviewers in the wild / expert
Eiji Ikeda
dblp:162/0789
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0000-0003-0420-4564ORCID · 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 · 44% Human-AI interaction · 44% Human-robot interaction · 13% |
Topics — the 1 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Learning and educational technologies
skill training |
1.0 | 1 | 2026 | ViSTAR: Virtual Skill Training with Augmented Reality with 3D Avatars and LLM coaching agent · CHI 2026 |
Methods — techniques the papers use, named apart from their topics
large language model · 1.0behavioral skills training framework · 1.03d motion reconstruction · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | ViSTAR: Virtual Skill Training with Augmented Reality with 3D Avatars and LLM coaching agentabstractWe present ViSTAR, a Virtual Skill Training system in AR that supports self-guided basketball skill practice, with feedback on balance, posture, and timing. From a formative study with basketball players and coaches, the system addresses three challenges: understanding skills, identifying errors, and correcting mistakes. ViSTAR follows the Behavioral Skills Training (BST) framework-instruction, modeling, rehearsal, and feedback. It provides feedback through visual overlays, rhythm and timing cues, and an AI-powered coaching agent using 3D motion reconstruction. We generate verbal feedback by analyzing spatio-temporal joint data and mapping features to natural-language coaching cues via a Large Language Model (LLM). A key novelty is this feedback generation: motion features become concise coaching insights. In two studies (N=16), participants generally preferred our AI-generated feedback to coach feedback and reported that ViSTAR helped them notice posture and balance issues and refine movements beyond self-observation. Chunggi Lee, Hayato Saiki, Tica Lin, Eiji Ikeda, Kenji Suzuki 0002, Chen Zhu-Tian, Hanspeter Pfister |
CHI | 4 |