Best practices and practical strategies for co-designing virtual reality with Indigenous peoples: A scoping review protocol
Bibliographic record
Abstract
Virtual reality (VR) is gaining traction in healthcare, education, and cultural sectors, from simulations in medical education to immersive museum experiences. Recently, VR has emerged as a powerful tool for Indigenous cultural preservation, language revitalization, and storytelling, offering immersive ways to safeguard knowledge and strengthen community connections. However, despite VR's potential to support Indigenous self-determination, little is known about the extent of Indigenous leadership, engagements, and settler-Indigenous collaborations in VR development. There is a critical need to examine how VR can be ethically and meaningfully co-designed with Indigenous communities to ensure cultural integrity, respect for Indigenous knowledge systems, and equitable participation in technological innovation. Thus, this scoping review aims to identify practical strategies and best practices for co-designing VR with Indigenous communities. In accordance with the JBI methodology, we will conduct a comprehensive search across seven electronic databases, including MEDLINE (EBSCOhost), Scopus, Web of Science, ACM Digital Library, IEEE Xplore, Compendex (Engineering Village), and ProQuest Dissertations and Theses Global (ProQuest). Google Scholar will also be searched for grey literature sources. Eligible studies will focus on Indigenous populations (Population) and fully immersive VR co-design (Concept) across various contexts. Studies that do not discuss the design process will be excluded. Two independent reviewers will conduct literature screening, data extraction, and analysis, with findings synthesized narratively and presented in a structured charting table. The results will be disseminated through a peer-reviewed journal publication and shared with relevant community partners to support knowledge translation and application.
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 | 0.164 | 0.150 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.038 | 0.022 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".