Human intelligence-based metaverse for co-learning of students and smart machines

Lee, Chang-Shing; Wang, Mei-Hui; Reformat, Marek; Huang, Shih-Chieh

doi:10.1007/s12652-023-04580-2

Cited by 10 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…19 [28], [30], [33], [38], [51], [52], [55][56][57][58], [62], [63], [66], [70], [72], [75], [78], [79], [81] Table 4 demonstrates the potential for experiential learning, collaborative learning, cooperative learning, meaningful learning, explicit learning, and emotional learning within the educational sector, benefiting both students and teachers [8]. This is consistent with the findings of this research.…”

Section: Discussionsupporting

confidence: 85%

“…Uses the knowledge you have to learn more 27 [30], [31], [34][35][36][37][38][39][40][41][42][43], [46], [48][49][50][51], [54], [55], [61], [63], [72], [73], [77], [78], [82], [83] 5 Explicit learning He wants to learn and selectively chooses what he considers relevant. 23 [28], [30], [31], [33], [38], [43], [51], [55][56][57][58], [62], [63], [66], [67], [70], [72], [73], [75], [76], [78], [79], [81]…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Benefits of Metaverse Application in Education: A Systematic Review

Flores-Castañeda,

Olaya-Cotera,

Iparraguirre-Villanueva

2024

Int. J. Eng. Ped.

View full text Add to dashboard Cite

The COVID-19 pandemic has brought about significant changes in people’s lifestyles, with the educational sector being one of the most reliant on technology to facilitate the teaching and learning process. In this literature review, a search for articles related to the metaverse in education, published in 2022 and 2023, has been conducted across six databases: Scopus, EBSCO Host, ScienceDirect, Taylor & Francis Online, IEEE Xplore, and Springer. The PRISMA methodology was used to analyze and systematize the manuscripts found. The aim of this research was to examine how integrating the metaverse into education can enhance educational accessibility and equity by enabling students to utilize virtual learning resources and opportunities. In addition, they can engage in interactions with others to learn and create interactive content during the teaching and learning process. This requires a commitment from the student because a connection between the student and the machine will be established through the use of emerging technologies. These technologies offer unique opportunities to enhance teaching quality, broaden access to education, and prepare individuals for an increasingly digital and evolving world. The analysis identified 14 emerging technologies: artificial intelligence, cloud computing, big data, Internet of Things, blockchain, augmented reality, extended reality, virtual reality, 5G, EON-XR, digital twins, 3D virtual reality, and immersive virtual reality. These technologies offer immersion (simulation of a real world in a virtual world), interactivity (interaction with different people), improvement of the educational environment (innovative presentation of content), and motivation for learning (capturing attention). When it comes to the different types of learning, there are six categories: experiential (based on experience), collaborative (involving a guide to lead the process), cooperative (involving teamwork), significant (building on existing knowledge), explicit (self-directed learning), and emotional (involving the regulation of emotions).

show abstract

Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Benefits of Metaverse Application in Education: A Systematic Review

Flores-Castañeda,

Olaya-Cotera,

Iparraguirre-Villanueva

2024

Int. J. Eng. Ped.

View full text Add to dashboard Cite

show abstract

“…Conversely, in the span from 2017 to 2024, Human and Machine Co-Learning indicates a shift away from competition towards a partnership and shared learning between humans and machines (Lee et al, 2020). This transition also introduces diverse types of learning, enriching the educational experiences across different age groups and enabling young learners to become acquainted with CI as it integrates with QCI (Lee et al, 2023b;Lee et al, 2024a). Once configured, the co-learning between the CI knowledge model based on HI as well as the machine model based on MI can be successfully established.…”

Section: Quantum CI Model Applications For Human-machine Interaction ...mentioning

confidence: 99%

Integrating Quantum CI with Generative AI for Taiwanese/English Co-Learning: TAIDE-based Knowledge Graph Construction and Multimodal Data Transformation

Lee,

Wang,

Chen

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

This paper proposes a Quantum Computational Intelligence (QCI) model integrated with Generative Artificial Intelligence (GAI) for Taiwanese/English language co-learning applications within human-machine interactions. The QCI model comprises two main phases: human-machine interaction and data processing for quantum circuit generation and real-world applications. During the human-machine interaction phase, a synergy between Human Intelligence (HI) and Machine Intelligence (MI) enables young students to gain familiarity with CI that converges with QCI. The second phase involves data processing, which encompasses stages of data preprocessing, analysis, and evaluation. The methodology is applied to two distinct applications: 1) Application 1 focuses on constructing a knowledge graph using the Ollama platform and the TAIDE model—a Trustworthy AI Dialogue Engine developed by the Taiwanese government based on the LLaMa 2 model. 2) Application 2 addresses the GAI images to text/voice, and text/voice to GAI images, depending on the type of Taiwanese/English data collected. Subsequently, the QCI model is refined through Particle Swarm Optimization (PSO) and Genetic Algorithm Neural Networks (GANN). Moreover, a Quantum Fuzzy Inference Mechanism (QFIM) is integrated to enhance the QCI model’s capability in creating a quantum circuit. The experimental results suggest that the QCI model significantly enhances human-machine collaboration. Looking forward, we plan to extend the QCI model to reach more young learners.

show abstract