Home // CENTRIC 2025, The Eighteenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services // View article

Identification of Design Recommendations for Augmented Reality Authors in Corporate Training

Authors:
Stefan Graser
Martin Schrepp
Stephan Böhm

Keywords: Augmented Reality (AR); Software Requirements Engineering; AR Design Recommendations; Corporate Training (CT); Natural Language Processing (NLP); Semantic Textual Similarity (STS); Sentence Transformers (SBERT).

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.

Pages: 9 to 17

Copyright: Copyright (c) IARIA, 2025

Publication date: September 28, 2025

Published in: conference

ISSN: 2308-3492

ISBN: 978-1-68558-298-2

Location: Lisbon, Portugal

Dates: from September 28, 2025 to October 2, 2025