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Human intelligence-based metaverse for co-learning of students and smart machines
This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998022/ https://www.ncbi.nlm.nih.gov/pubmed/37228697 http://dx.doi.org/10.1007/s12652-023-04580-2 |
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author | Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui |
author_facet | Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui |
author_sort | Lee, Chang-Shing |
collection | PubMed |
description | This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English. |
format | Online Article Text |
id | pubmed-9998022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-99980222023-03-10 Human intelligence-based metaverse for co-learning of students and smart machines Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui J Ambient Intell Humaniz Comput Original Research This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English. Springer Berlin Heidelberg 2023-03-09 2023 /pmc/articles/PMC9998022/ /pubmed/37228697 http://dx.doi.org/10.1007/s12652-023-04580-2 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Human intelligence-based metaverse for co-learning of students and smart machines |
title | Human intelligence-based metaverse for co-learning of students and smart machines |
title_full | Human intelligence-based metaverse for co-learning of students and smart machines |
title_fullStr | Human intelligence-based metaverse for co-learning of students and smart machines |
title_full_unstemmed | Human intelligence-based metaverse for co-learning of students and smart machines |
title_short | Human intelligence-based metaverse for co-learning of students and smart machines |
title_sort | human intelligence-based metaverse for co-learning of students and smart machines |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998022/ https://www.ncbi.nlm.nih.gov/pubmed/37228697 http://dx.doi.org/10.1007/s12652-023-04580-2 |
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