Nian-Shing Chen

1. Recent advances in artificial intelligence (AI) have prompted educators and researchers to reconsider fundamental assumptions about teaching, learning, and instructional design. As AI tools beco...

Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse.

This study aims to investigate whether there are differences in the intentions to adopt maker education between in-service and pre-service teachers. It also seeks to identify the key factors affecting their intentions, providing insights for tailoring professional development (PD) training programs to meet the needs of teachers at different career stages. A hybrid maker-based PD program was designed and offered to both in-service and pre-service teachers. The participants' behavioral intentions to adopt maker education were assessed after the PD program using an extended Theory of Planned Behavior model. The intentions of in-service and pre-service teachers were examined, and the key factors affecting their intentions were identified using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results showed that pre-service teachers had significantly lower intentions to adopt maker education compared to in-service teachers. For pre-service teachers, attitude was the key factor influencing their intentions, while for in-service teachers, subjective norms played a crucial role. These findings highlight the importance of tailored PD for teachers at different career stages.

This study aims to create and validate an evaluation tool that assesses the learning effectiveness of extended reality applications (e.g., augmented reality, virtual reality, mixed reality, and the metaverse). Six research questions were formulated to guide this study. The Extended Reality in Education (XREd) questionnaire consists of 50-items derived from 25 existing and validated questionnaires and surveys. The tool uses a 1–5 Likert scale and its items correspond to 15 factors, which are categorized into 5 dimensions. Both the factors and dimensions are supported by related theoretical aspects and background, and are grouped as follows: (1) System usability: Ease of use and Instructional clarity, (2) Learner’s basic psychological needs: Enjoyment, Autonomy, Self-efficacy, and Social interaction, (3) Learner’s learning state: Flow, Presence, and Immersion, (4) Learner’s learning performance: Learning outcomes, Learning motivation, and Learning engagement, (5) Learner’s learning perceptions: Perceived usefulness, Learning preferences alignment, and Behavioral intention. To validate the tool, descriptive statistics and reliability tests, validity tests, and factor analyses, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modelling (SEM) were used. An extended reality application was developed and applied in higher education settings over the course of a semester. The experiment data was used to validate the created evaluation tool, which was then used to evaluate the effectiveness of the developed XR application and to answer the 6 research questions set to be explored. The results confirmed the validity of the XREd questionnaire and its effectiveness in evaluating extended reality applications. Additionally, its potential to be modularly integrated to evaluate non-extended reality learning applications was highlighted. Finally, the developed application was positively assessed by students, who regarded it as an effective learning tool that can support and enrich their learning experience and improve learning outcomes.

The article analysed the main reflections presented by experts from different countries in the area of AI in education, who participated in the round table debate “E-Learning and Enhancing soft skills: Contemporary Models of Education in the era of Artificial Intelligence”. The agenda included three topics: 1) E-learning and enhancing soft skills: contemporary models of education in the era of artificial intelligence; 2) Good practice examples using AI in education for soft skills development; 3) AI – perspectives and challenges of its use in education. The conclusions of the round table lead to the reflection that integrating AI and robots can enhance soft skills development and revolutionize education by creating personalized, efficient, and inclusive learning environments. AI offers benefits like task automation, real-time feedback, and support for diverse needs. However, challenges such as equity, privacy, bias, and ethical concerns must be addressed. Success requires adequate teacher training, a focus on equity and accessibility, ethical considerations, strong data protection, and maintaining human interaction and creativity. By addressing these challenges thoughtfully, education systems can fully harness AI’s potential to improve learning outcomes.