The role of metaverse in training and educational context: Potentialities, use-cases, and research directions
Bibliographic record
Abstract
The rapid rise of smart devices and advancements in mobile computing, machine learning, and artificial intelligence have set the stage for the metaverse—a shared, immersive virtual world where people can interact through dynamic digital environments and avatars. This innovation is poised to transform various sectors, with education standing out as a key area of impact. In education, the metaverse promises to revolutionize learning by enabling students and instructors to engage in immersive virtual environments. Students can explore historical events, conduct experiments in virtual labs, or develop real-world skills in risk-free simulations. Educators can deliver adaptive and interactive lessons tailored to individual needs, creating more engaging and effective experiences. However, realizing the metaverse’s potential requires overcoming significant challenges, such as improving technology scalability, ensuring seamless user experiences, and addressing data privacy concerns. This paper examines the metaverse’s potential in education, highlights enabling technologies, and outlines key research directions to overcome current barriers.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".