Designing immersive stories with novice VR creators: a study of autobiographical VR storytelling during the COVID-19 pandemic
Bibliographic record
Abstract
Virtual reality (VR) is increasingly being used as a tool for eliciting empathy and emotional identification in fact-based stories. However, it may not be clear whether VR stories authentically deliver the protagonists’ perspectives if the works are not created by or with the protagonists themselves. Therefore, it is crucial for the VR community to explore effective methods for democratizing VR storytelling, and to support novice VR designers in creating autobiographical stories. In this paper, we report findings from a collaborative design research project that aimed to create autobiographical stories with novice VR designers who lacked experience in VR storytelling. We collaborated with university students in Canada to design eight individual VR stories that expressed each student’s experiences of lockdown, during the early stages of the COVID-19 pandemic. We conducted interviews with the students to understand how VR contributed to conveying their individual experiences. Our findings demonstrate how immersive VR can be used as a meaningful tool for sharing autobiographical stories by delivering the character’s feelings, creating a sense of confinement and isolation, expressing inner worlds, and showing environmental details. Our discussion draws attention to the significance of careful camera positioning and movement in VR story design, the meaningful use of limited interaction and disorienting components, and the balance between spatial and temporal information in a three-dimensional environment. Our study highlights the potential of VR as an autobiographical storytelling tool and demonstrates how VR stories can be created through iterative collaboration between VR experts and novices.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".