Interactive Storytelling for Immersive Media, Augmented Manufacturing, and Digital Healthcare
Bibliographic record
Abstract
The article explores interactive storytelling in augmented and virtual reality. It is based on the research project Immersive Media Lab, bridges the gap between technological developments and their application, and aims to combine art, economy, and science. Due to their increased availability, virtual and immersive technologies have not only seen a resurgence in their popularity but also offer intriguing new perspectives regarding their use in different contexts. Consequently, the notions of interactivity and immersion also play significant roles in the study of storytelling in conjunction with virtual and augmented reality. The Immersive Media Lab has employed state-of-the-art AR/VR technology in five use cases: VR Interaction, Artistic Motion Tracking in AR/VR, Audio AR for Industry, AR/VR Interfaces for Industry, and AR Education for Patients. All of them, but one, developed prototypical applications related to the central concept of interactive storytelling. Furthermore, each use case addresses three distinct dimensions of storytelling: the production of a coherent story, the collection and transfer of knowledge, and the specificity of the technology used. Covering the areas of Creative Media, Smart Manufacturing, and Healthcare leads to a particular research design, as each area is connected to different research contexts and comes with appliances of immersive media. The article focuses on various theoretical inputs regarding interactive and immersive storytelling in general and relates them to the academic background of the five use cases. Furthermore, each use case defined a story in its context, the purpose of its narration, and its media-specific context. Finally, the paper reflects on the outcomes of the use cases and outlines their potential for future applications of interactive storytelling in immersive media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".