Co-Creation

Developing Virtual Reality in Learning Contexts

Developing Virtual Reality in Learning Contexts

Navigating efficiency, content relevance and scalability
Stella Kanatouri ORCID Icon, Oliver Sosna ORCID Icon, Alexander Kulik, Sina C. Truckenbrodt ORCID Icon, Friederike Klan ORCID Icon, Christian Erfurth ORCID Icon
While virtual reality can facilitate hands-on learning, its development faces barriers, including high costs and time demands and scalability challenges. This article presents two case studies that illustrate strategies for overcoming such barriers when training the next generation of skilled workers in environmental technologies. By examining approaches for streamlining development and increasing content relevance and scalability, we highlight lessons learned for future practice. We conclude by envisioning a future in which educational institutions can flexibly and cost-effectively prototype virtual reality in learning contexts, ensuring alignment with curricular goals and learners’ needs.
Industry 4.0 Science | Volume 42 | Edition 3 | Pages 26-34 | DOI 10.30844/I4SE.26.3.3
Collaborative Robots in Production Environments

Collaborative Robots in Production Environments

Employee qualification and acceptance for human-machine interaction
Tobias Wienzek, Mathias Cuypers ORCID Icon
The introduction of new technologies poses a major challenge, especially for small and medium-sized enterprises (SMEs). At the same time, SMEs must rise to this challenge in order to keep pace technologically and economically. Employee acceptance is an important factor in ensuring that both the introduction and the long-term use of a technology are successful. At the same time, the introduction process also has a central influence on acceptance in the long term. This article uses the implementation of collaborative robotics as an example for examining such an introduction process, identifying the key factors that influence employee acceptance and the important role played by advanced employee training. It serves to highlight how the introduction process and employee training are seamlessly interlinked.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 14-21 | DOI 10.30844/I4SE.26.2.14
Artificial Intelligence in Visual Quality Control

Artificial Intelligence in Visual Quality Control

Using intelligent algorithms to improve product quality, increase efficiency and reduce costs
Stefanie Horrmann
Manufacturing companies must work economically while delivering quality - in some industries with a zero-defect tolerance. Quality control often is carried out manually and with a time delay, thus errors can only be corrected at a late stage. Using artificial intelligence (AI), visual quality control can be automated, carried out in real time and integrated into the production process - making it more accurate, efficient and cost-effective. A case example shows the advantages of tackling AI issues in interdisciplinary teams with partners.
Industrie 4.0 Management | Volume 36 | 2020 | Edition 2 | Pages 57-60