Identification of Design Recommendations for Augmented Reality Authors in Corporate Training
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916868696047616 |
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| author | Graser, Stefan Schrepp, Martin Böhm, Stephan |
| author_facet | Graser, Stefan Schrepp, Martin Böhm, Stephan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identification of Design Recommendations for Augmented Reality Authors in Corporate Training Graser, Stefan Schrepp, Martin Böhm, Stephan Human-Computer Interaction Software Engineering 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. |
| title | Identification of Design Recommendations for Augmented Reality Authors in Corporate Training |
| topic | Human-Computer Interaction Software Engineering |
| url | https://arxiv.org/abs/2507.21722 |