Extended reality for education: Mapping current trends, challenges, and applications
Bibliographic record
Abstract
The advancements in 5G technology and Artificial Intelligence (AI) have accelerated the integration of immersive technologies such as Extended Reality (XR) into educational practices. There is a notable scarcity of studies focusing specifically on the applications and impact of XR in academic settings. Most existing research has concentrated on AR and VR, leaving a gap in understanding the full potential of XR. Addressing these gaps and challenges is crucial for harnessing the full potential of XR in education. This study aims to map and analyze the applications, trends, and educational challenges of XR technology. This study conducts a bibliometric analysis covering XR's application in education from 2018 to 2023, analyzing 32 articles from Scopus sources. Key findings highlight XR's annual growth in research publications, with significant contributions from the United States, China, and Canada. XR enriches education by facilitating immersive simulations, real time interaction with virtual objects, and spatial manipulation in three dimensions. It fosters presence and embodiment in virtual environments, supports practical training through realistic simulations, enhances multi-sensory engagement, promotes collaborative learning environments, and improves accessibility for diverse learners. The main challenges of XR technology include high costs, technical hurdles, regulatory issues, infrastructure limitations, and the need for digital literacy and skills. Addressing these challenges, collaborative efforts among educators, researchers, and industry stakeholders are required. Such collaboration is crucial for harnessing the full potential of XR technology to revolutionize education and prepare learners for a dynamic future.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".