LAUSR

Despite the proven benefits of augmented reality (AR) in human-centered training, its widespread adoption is hindered by industrial companies’ difficulties in justifying the required investments due to unclear financial implications. Thus, having a tool to economically evaluate AR solutions for training is crucial for companies aiming to invest in educational programs. Accurate investment estimations for AR-based training require detailed analysis and data estimation, especially for human-centered applications. In this work, we propose a model to evaluate the economic feasibility of investing in AR-based industrial training, particularly focusing on the costs and benefits of a human-centered approach. This model links AR-related and human-centric factors to the cost and benefit variables used in standard models to compute common economic indices. A comprehensive methodology has been developed to support the model’s creation and validation based on a case study approach. Functionalities to support data estimation and alternative selection have been integrated into the model to enhance the reliability of its outcomes. The research findings and case study results advance training strategies and the implementation of AR applications in industry. They offer companies operating in complex industrial contexts a simple-to-use tool for evaluating the costs/benefits ratio and thus overcoming common technical and organizational barriers by clarifying financial implications, which are often a significant obstacle to the widespread adoption of new technologies like AR, especially when considering human-centric applications.