Examining Pair Dynamics in Shared, Co-located Augmented Reality Narratives
Bibliographic record
Abstract
Augmented reality (AR) allows users to experience stories together in the same physical space. However, little is known about the experience of sharing AR narratives with others. Much of our current understanding is derived from multi-user VR applications, which can differ significantly in presence, social interaction, and spatial awareness from narratives and other entertainment content designed for AR head-worn displays. To understand the dynamics of multi-user, co-located, AR storytelling, we conducted an exploratory study involving three original AR narratives. Participants experienced each narrative alone or in pairs via the Microsoft Hololens 2. We collected qualitative and quantitative data from 42 participants through questionnaires and post-experience semi-structured interviews. Results indicate participants enjoyed experiencing AR narratives together and revealed five themes relevant to the design of multi-user, co-located AR narratives. We discuss the implications of these themes and provide design recommendations for AR experience designers and storytellers regarding the impact of interaction, physical space, spatial coherence, and narrative timing. Our findings highlight the importance of exploring both user interactions and pair interactions as factors in AR storytelling research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".