Virtual and augmented reality in the libraries: Situation analysis, hotspots and new directions
Bibliographic record
Abstract
The integration of Virtual Reality (VR) and Augmented Reality (AR) technologies into library presents a transformative opportunity to enhance user experiences, promote innovative learning, and improve access to information. The purpose of this research is to examine the state of research on the virtual and augmented reality in the library including understanding the hotspots and new research directions. The study was conducted using bibliometrics approach based on data collected from Scopus database. The retrieval yielded 4001 items and covered the period 1973 and 2023. The years 2020–2023 mark the most substantial period of activity. The keywords cluster in four categories namely - technological components and applications, and computational methods, human and social aspects of VR/AR use, and interface and user interaction. The research hotspots are (1) Core Technologies, (2) Applications, and (3) Research Methodologies and Trends while the three emerging areas are - Emerging Technologies and Methods, Class 2: User Interaction and Interface Design, Class 3: Niche Applications in Libraries. The integration of VR and AR into library systems demonstrates their evolution from experimental concepts to practical tools, enhancing user engagement and supporting academic, cultural, and educational functions. The study of VR and AR in libraries reveals a clear trajectory of growth and maturity, with research efforts expanding significantly, particularly after 2000. Advances in technology and evolving user needs—such as remote access demands during the COVID-19 pandemic—have driven innovation, shifting the focus from foundational studies to applied, mature research in library settings.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.038 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| 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".