This study proposes education on creating virtual art museums using metaverse technology to broaden the understanding of art museums. It investigates the effect of creating a virtual art museum using Roblox Studio, a metaverse platform, on the acquisition of knowledge about art museums, artists, and artworks and the ability to create metaverse content. This study selected the Moonshin Art Museum (MAM), the art museum to be created in the metaverse space, as its local cultural space. Fifteen students participated in a creation workshop to learn to create virtual spatial content using Roblox Studio and also visited an actual art museum. The students were then assigned to architecture, artwork, avatar, and content teams and collaborated with one another. To evaluate the activities, students filled out pre- and post-questionnaires containing items about the MAM, the sculptor Moonshin, and Moonshin’s artwork, as well as the ability to produce metaverse content. Findings showed that creating a virtual art museum through a metaverse platform facilitates the acquisition of spatial knowledge about art museums as well as information about artists and artworks. In addition, collaboration not only helped in the creation task but also identified and solved technical difficulties and improved creative abilities. These results suggest possibilities for using metaverse technology in delivering education regarding virtual art museums.

In 360-degree virtual reality (VR) videos, users possess increased freedom in terms of gaze movement. As a result, the users’ attention may not move according to the narrative intended by the director and miss out on important parts of the narrative of the 360-degree video. Therefore, it is necessary to study a directing technique that can attract user attention in 360-degree VR videos. In this study, we analyzed the directing elements that can attract users’ attention in a 360-degree VR video and developed a 360 VR eye-tracking system to investigate the effect of the attention-attracting elements on the user. Elements that can attract user attention were classified into five categories: object movement, hand gesture, GUI insertion, camera movement, and gaze angle variation. We developed a 360 VR eye-tracking system to analyze whether five attention-attracting elements influence the user’s attention. Based on the eye tracking system, an experiment was conducted to analyze whether the user’s attention moves according to the five attention-attracting elements. Based on the experimental results, it can be seen that ‘hand gesture’ attracted the second most attention shift of the subjects, and ‘GUI insertion’ induced the smallest shift of attention of the subjects.

In virtual reality (VR), users underestimate distances between themselves and objects relative to reality. Such issues can negatively impact users’ spatial perception and interactions in VR environments by affecting elements of the VR user experience, such as presence. Therefore, enhancement of depth perception should be studied to improve the VR user experience. In this study, an eye-tracking-based depth perception visualization interface system is proposed to enhance depth perception and improve presence in VR. The visualization interface system consists of a three-dimensional (3D) depth interface or a two-dimensional (2D) depth interface, depending on the interface depth. Experiments on depth perception and presence were conducted to analyze whether the proposed visualization interface affects user experience. An analysis of the experimental results suggests that the proposed visualization interface system can benefit depth perception and presence with the 3D depth interface potentially being more effective than the 2D depth interface.

Virtual reality (VR) provides immersive environments that resemble real-world consumption settings, enabling realistic analysis of consumer responses to advertisements. Therefore, VR has been increasingly adopted in marketing. Visual attention is a key indicator of advertising effectiveness, and neuromarketing approaches using eye-tracking are widely used to overcome the limitations of self-report measures by providing objective insights into attentional processes. However, most previous studies have focused on static retail environments, leaving a research gap in understanding advertising effectiveness in dynamic road traffic contexts. Guided by selective attention theory, this study addresses this gap by integrating VR and eye-tracking to examine how advertisement placement under different traffic conditions influences visual attention and recall. A real-time eye-tracking measurement system was developed, and fixation duration, fixation count, and recall were used as evaluation metrics. The results showed significant differences across advertisement placement types. Advertisements positioned in front of buildings during stops elicited the highest levels of visual attention and recall, indicating that attention is greater when users are stationary than when riding. These findings indicate that cognitive resources shift from traffic-related tasks to advertisements as cognitive load decreases, highlighting the effectiveness of integrating VR and eye-tracking to objectively evaluate advertising outcomes in dynamic environments.

Virtual reality (VR) technology provides a high sense of immersion and presence to users and can enhance the engagement and performance of learning. However, the VR learning environment introduces more complex audio–visual stimuli than the traditional multimedia learning environment. These excessive stimuli cause negative effects such as distraction and cognitive overload. To minimize these negative impacts and improve the learning environment, we must evaluate learners’ cognitive states under the VR environment. Cognitive states can be evaluated subjectively (e.g., through questionnaires) or objectively (e.g., using biometric signals). Subjective and objective methods must be used simultaneously, and correlations between them must be analyzed for quantifying objective measures. The accurate detection of cognitive states is challenging for traditional statistical analysis methods, necessitating the exploration of artificial intelligence (AI) techniques that can classify cognitive states. This study develops a VR learning experience evaluation system based on eye-tracking data. Cognitive states during VR learning are classified as cognitive overload, immersion, and distraction. Correlations between each cognitive state and eye-tracking metrics are evaluated, and the possibility of cognitive-state quantification is discussed. An LSTM-based model developed in this study classified cognitive states from eye-tracking data with moderate accuracy (75.60%) under a subject-independent validation setting.