Augmented reality-guided brushwork training
Enhancing precision and cultural cognition in traditional Chinese painting education
Bibliographic Data
| ID | 21452264 |
|---|---|
| Authors | Yuefeng Shi (0000-0002-9683-6085, Lu Xun Academy of Fine Arts), Hongxu Bian (Lu Xun Academy of Fine Arts, corresponding author) |
| Year | 2026 |
| Pages | 1-18 |
| Publication date | 2026-03-04 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Behaviour and Information Technology (JOURNAL) |
| Journal identifiers | ISSN: 0144-929X • E-ISSN: 1362-3001 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/0144929x.2026.2635520 |
| OpenAlex | W7133939893 |
| Language | EN |
| References cited | 35 |
Traditional Chinese painting (Guóhuà) demands precise motor control and culturally embedded cognition. This study introduces an augmented reality (AR) system that provides real-time feedback on brushwork techniques in university-level Chinese painting courses. Ninety-six visual arts undergraduates participated in an eight-week intervention, with one group using the AR system and the other receiving traditional instruction. The AR system delivered motion – and pressure-driven overlays via head-mounted displays or smartphones. Primary endpoints included stroke fidelity (GSPR total and subscales), compositional accuracy (centroid displacement, alignment error, spatial density variance), engagement, usability, symbolic cognition, and skill transfer. Mixed-effects models, adjusted for baseline technology familiarity and workload, revealed significant improvements in the AR group post-intervention (GSPR total: +8.5 vs +1.9; composition: centroid −6.3 vs −1.9 mm; alignment −4.5° vs −1.2°; density −2.3 vs −0.5 px/cm2; all p < 0.001). Symbolic cognition was higher (91.3% vs 76.8%) with faster responses (1824 vs 2351 ms), and engagement was longer (79.2 vs 67.4 min). AR-guided training demonstrated sustained advantages at Week 12, indicating robust skill acquisition and transferability. This approach offers a scalable solution for integrating culturally sensitive digital pedagogy into arts education. The complete source code of the Dynamic Brushwork Embodied Visualisation Model (DBEVM) and associated datasets have been published on GitHub at https://github.com/Shiyuf24/Guohua-AR-DBEVM
Chinese painting · Cognition · Cultural diversity · Narrative · Painting · Aesthetic Perception and Analysis · Augmented Reality Applications · Virtual Reality Applications and Impacts
| Citation velocity | historical |
|---|---|
| Highly cited | No |