Identification of Design Recommendations for Augmented Reality Authors in Corporate Training

TL;DR

This paper identifies and analyzes practical augmented reality design recommendations specifically for corporate training, using a multi-method approach to extend and classify a dataset of recommendations, aiding AR application development.

Contribution

The study provides an updated, classified dataset of 597 AR design recommendations tailored for corporate training, with insights into their relevance and applicability.

Findings

597 recommendations classified into 84 topics

32 topics with 284 statements relevant for AR in CT

Extended dataset supports AR UX measurement approaches

Abstract

Innovative technologies, such as Augmented Reality (AR), introduce new interaction paradigms, demanding the identification of software requirements during the software development process. In general, design recommendations are related to this, supporting the design of applications positively and meeting stakeholder needs. However, current research lacks context-specific AR design recommendations. This study addresses this gap by identifying and analyzing practical AR design recommendations relevant to the evaluation phase of the User-Centered Design (UCD) process. We rely on an existing dataset of Mixed Reality (MR) design recommendations. We applied a multi-method approach by (1) extending the dataset with AR-specific recommendations published since 2020, (2) classifying the identified recommendations using a NLP classification approach based on a pre-trained Sentence Transformer model, (3) summarizing the content of all topics, and (4) evaluating their relevance concerning AR in Corporate Training (CT) both based on a qualitative Round Robin approach with five experts. As a result, an updated dataset of 597 practitioner design recommendations, classified into 84 topics, is provided with new insights into their applicability in the context of AR in CT. Based on this, 32 topics with a total of 284 statements were evaluated as relevant for AR in CT. This research directly contributes to the authors' work for extending their AR-specific User Experience (UX) measurement approach, supporting AR authors in targeting the improvement of AR applications for CT scenarios.