The metaverse, a rapidly evolving research area, is a virtual, interconnected universe that seamlessly integrates digital and physical realms, facilitating immersive experiences, social interaction, and economic activities across various fields, including computer science, business, and education. Despite its potential to transform current pedagogies and learning experiences through engaging, interactive, and fascinating educational techniques, research is limited in exploring its application, usability, and user experience, particularly for Thai undergraduate students in learning computer engineering courses. In response, we designed and developed an innovative metaverse-based learning system called the AIIS collaborative learning interface, tailored to European undergraduate students. While the original intent of AIIS was to cater to medical students, in this study, our objective was to assess its usability and applicability for computer science and engineering students, specifically focusing on the Asian demographic. After an initial pilot with European students in their local context, the research was extended to Thailand, where 21 Thai undergraduate students evaluated the platform’s usability. The observations from this study indicate that AIIS provides a dynamic, user-friendly learning environment. Nevertheless, the evaluation process unveiled some usability flaws that offer informative directions for future enhancements. Drawing from our observations, we formulated usability guidelines to guide designers and developers toward crafting more efficient metaverse systems, particularly those intended for educational use. Our results also underline the considerable promise that metaverse technologies hold for advancing higher education globally.

Methods based on simulation pedagogy are widely used to practice hands-on skills in safety environment. The usability of an EEG simulator on clinical neurophysiology course was evaluated. Second-year biomedical laboratory science students (N = 35) on this course were included in the study. They were divided into three groups. Two groups used the EEG simulator with different feedback modes and one group did not use the simulator. Results were expected to reveal a correlation between user experience and learning outcomes. This study made used of a mixed method study design. During the study, students were asked to keep a learning diary throughout the course on their experience. Diaries were analyzed qualitatively based on content analyses. Quantitative analyses based on an UX questionnaire that measures classical usability aspects (efficiency, perspicuity, dependability) and user experience aspects (novelty, stimulation) and the students’ feelings to use simulator. The quantitative data was analyzed using SPSS^TM software. The quantitative and qualitative analyses showed that the use of the EEG simulator, which was evaluating teaching–learning process, has an extra benefit in clinical neurophysiology education and students felt that the simulator was useful in learning. The simulation debriefing session should be followed by a full theoretical and practical session. Students compared their learning from the simulator with that of the actual placement which fosters the reflective practice of learning, again deepening the understanding of the EEG electrode placement and different wave patterns.

Arm robot technology has been introduced in manufacturing and other industries to improve worker safety and productivity. Although arm robot technology tends to automate tasks, some tasks are complex. Such tasks require human operators. However, many studies focus on this issue and propose methods for operating arm robots; various issues still need to be solved, such as the possibility of unintended movements and the difficulty of intuitive operation. We propose an intuitive arm robot manipulation method using augmented reality and input devices that prevent unintended movements and enable intuitive operation. This system enables the user to operate an actual arm robot in a remote location by manipulating a model of the arm robot presented in augmented reality. The system also controls the position and posture of the arm robot head and the opening and closing of the gripper by using a hand and a handheld input device to control each joint angle.