Towards Integrated Learning and Assistance Systems: A Framework for Technology Selection and Design

Abstract

In the era of Industry 4.0, advanced assistance technologies have permeated manufacturing processes, yet the absence of systematic implementation methods remains a challenge. This research presents a multidimensional framework developed through inductive reasoning, encompassing technological capabilities and instructional considerations. Its primary objective is to guide the development of context-specific assistance solutions, shifting the focus from automated solutions to thoughtful technology selection and implementation. The framework's application has the potential to elevate stakeholder design processes through interactive visualizations and experiential anchoring. Notably, the adoption of cognitive assistance systems within manufacturing is on the rise, driven by the maturation of virtual and augmented reality solutions. However, a notable gap exists in systematic approaches for selecting and designing these technologies. This paper addresses this gap by proposing a comprehensive framework grounded in an inductive approach. This research seeks to enhance awareness among decision-makers in companies regarding essential selection and decision criteria. Furthermore, it offers guidance for a systematic and participatory implementation process. The research results underscore the importance of adopting such a framework in the industry 4.0 landscape, shedding light on the need for strategic technology integration. This study suggests that the proposed framework can serve as a valuable tool to facilitate informed decision-making and drive the successful implementation of assistance technologies in manufacturing processes.