The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Constructing an Edu-Metaverse Ecosystem: A New and Innovative Framework
Abstract
The Metaverse is a network of 3-D virtual worlds supporting social connections among its users and enabling them to participate in activities mimicking real life. It merges physical and virtual reality and provides channels for multisensory interactions and immersions in a variety of environments (Mystakidis, 2022). The Metaverse is considered the third wave of the Internet revolution, and it is built on new and emerging technologies such as extended reality and artificial intelligence. Research on the impact of the Metaverse on education exploded in 2022. Here, we explore learning across the Metaverse and propose a new and innovative theoretical framework by reviewing literature and synthesizing best practices in designing metaverse learning environments. This ecosystem consists of four major hubs: 1) instructional design and performance technology hub; 2) knowledge hub; 3) research and technology hub; and 4) talent and training hub. Common to all four hubs are the factors in the three wheels: 1) infrastructure, business industry, and communication; 2) technology access and equity; and 3) user rights, data security, and privacy policy. We believe that this framework can help guide emerging research and development on the applications of the Metaverse in education. We also hope this article can serve as a launch pad for the special issue on the Metaverse and the Future of Education supported by the IEEE Education Society.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In addition, education has now entered the era of Metaverse and artificial intelligence (Wang et al., 2022).”
From A systematic review of Stimulated Recall (SR) in educational research from 2012 to 2022 (2024) · cites this paper as Wang et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mystakidis, S. (2022) · Encyclopedia · doi:10.3390/encyclopedia2010031 · Cited by 1,977
Lee, L.-H., Braud, T., Zhou, P., Wang, L., Xu, D., Lin, Z., Kumar, A., Bermejo, C., & Hui, P. (2021) · arXiv · doi:10.48550/arxiv.2110.05352 · Cited by 1,088
Makransky, G., & Mayer, R. E. (2022) · Educational Psychology Review · doi:10.1007/s10648-022-09675-4 · Cited by 386
Lee, H., & Hwang, Y. (2022) · Sustainability · doi:10.3390/su14084786 · Cited by 345
Cited by
A systematic review of Stimulated Recall (SR) in educational research from 2012 to 2022
Zhai, X., Chu, X., Wang, M., Tsai, C.-C., Liang, J.-C., & Spector, J. M. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02987-6 · Cited by 47
Self-citationMetaverse ethics: exploring the social implications of the metaverse
Yasuda, A. (2025) · AI and Ethics · doi:10.1007/s43681-024-00507-5 · Cited by 27
Lazou, C., & Tsinakos, A. (2025) · Information · doi:10.3390/info16060482 · Cited by 10
Metadata
- Authors
- Wang, M., Yu, H., Bell, Z., & Chu, X.
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2022-12-01
- Volume
- 15
- Issue
- 6
- Pages
- 685-696
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W4312100473
- Cited by
- 271
- References
- 4
- FWCI
- 28.67
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.