Enhancing Craftsmanship: Evaluation of AR-Assisted Learning of Work Steps in Woodworking
Diana Grüger et al.
Abstract
This study addresses an unexplored intersection of vocational education and training (VET), motor skill learning, craftsmanship and the architecture, engineering and construction (AEC) industry as well as expertise sharing. Acquiring proficiency in craftsmanship involves not only theoretical knowledge but also a multitude of physical skills, such as chiselling, which are honed through repetition. Mastering these manual skills and combining them with knowledge of materials, such as wood, is essential for an effective executing of craftmanship. We developed an augmented reality (AR) prototype through the design case study approach to design a support for beginners, especially apprentices, in woodworking education where the connection between craftspeople and material is important for the end results. The prototype was evaluated by 28 apprentices inside and outside a woodwork shop setting. While participants praised the prototype’s 3D visualisations and interactive models, challenges in compatibility with woodwork shop environments were evident. Our findings highlight the importance of on-site evaluations in the craftsmanship context and the significance of direct interaction with real world materials. We provide a set of seven design guidelines derived from our empirical findings to inform the development of AR-based learning applications in craftsmanship. Our discussion opens up the question of the role of interactive 3D models for collaborative learning. The research extends AR’s impact in craftsmanship and AEC industry. Our study underscores the importance of expert knowledge in handling certain materials and the potential of AR to reshape work practices and visualisations, contributing to expertise sharing in this domain.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.