Transforming education and research with extended reality technologies: How virtual reality can shape the future of data interactions in earth and environmental sciences
Bibliographic record
Abstract
Virtual Reality (VR) technologies offer powerful tools to enhance information dissemination, introduce novel perspectives, and foster immersive learning experiences, while preserving the three-dimensional (3D) nature of data and its spatial and temporal relationships. Despite being available in various forms for over sixty (60) years, VR technology has seen limited mainstream adoption. Recent advances in computing power, improvements in VR development software, and the expansion of content creation tools have significantly enhanced the VR's accessibility and practicality. As a result, VR is emerging as a viable medium for collaborative scientific inquiry, pedagogy, and public engagement. Numerous academic disciplines, including medicine, education, and psychiatry, are at the forefront of utilizing VR in innovative and impactful ways that bolster the research processes, education, and scientific communication. Earth and environmental sciences have demonstrated a range of promising VR applications, particularly through immersive, data-driven simulations that enable scientists to analyze and interpret complex systems. These simulations also function as effective communication tools by presenting concepts in intuitive ways to non-experts, thereby deepening their understanding of the research questions at hand. However, many VR applications in earth and environmental sciences remain disconnected from routine workflows in research, education, and outreach, which limits their broader integration and sustained use. This review synthesizes a broad body of academic literature to identify effective practices, explore persistent challenges, and provide a comprehensive overview of VR implementation across disciplines, with a particular focus on earth and environmental sciences. Our thesis is that VR's realism, variable-isolation capabilities, and cost-effectiveness can be conducive to a major paradigm shift in research and education.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".