Yosuke Fukuchi

Controlling multiple agents in parallel is an important challenge in human–robot interaction, with growing applications in remote collaboration, telepresence, and multi-agent coordination. However, empirical work on distributed embodiment remains limited, particularly in scenarios where independently controlled avatars operate in distinct environments. To address this gap, we propose the concept of Dual Body Sensation (DBS): a novel theoretical construct of distributed embodiment, describing a state in which a single operator simultaneously experiences embodiment for two independent robots in distinct environments during their control. We conducted a within-subject experiment to investigate the cognitive feasibility of DBS using a 2×3 factorial design that varied visual presentation (independent view, superimposed view) and tactile presentation (no tactile, bilateral tactile, unified tactile). The results indicated that DBS emerged across all conditions and was significantly enhanced under the unified tactile condition. The combination of the superimposed view and unified tactile conditions also increased the sense of body ownership and the sense of agency, while the superimposed view condition reduced cybersickness and cognitive load. These findings provide empirical support for DBS and inform the design of future multi-agent interaction systems that support flexible distribution of bodily control and awareness.

Virtual Reality (VR) technology offers immersive experiences across various domains, but assessing user experience (UX) remains a challenge. Traditional methods, such as questionnaires, are time-consuming and fail to capture the complexity of user interactions, while automated approaches which leverage sensors and deep learning (DL) techniques show promise but have several architectural limitations. Existing frameworks often rely on data from contact-based physiological sensors. They also tend to overlook attributes of the visual stimuli, use simplistic multimodal fusion techniques, and focus on a single UX dimension, all of which reduce their ability to provide a comprehensive and accurate assessment of UX. This study proposes MUXAS-VR, a DL-based framework that overcomes these limitations by leveraging multimodal behavioral cues. These are seamlessly captured using embedded sensors in commercially available VR devices, eliminating the need for contact-based physiological sensors. MUXAS-VR integrates individual behavioral data with visual attributes, such as motion dynamics and visual complexity, to account for individual interactions and visual influences. Its Multimodal Fusion Module (MFM) selectively combines data from multiple modalities to enhance feature representation and predictive performance. Unlike conventional systems, MUXAS-VR evaluates multiple UX dimensions—cybersickness (CS), sense of presence (SOP), and overall satisfaction (OS)—to provide a comprehensive view of VR UX. To validate its effectiveness, we proposed a rigorous evaluation strategy across three scenarios—Missing Content, New Content, and New User—to ensure generalizability and practical applicability. Experimental results showed that multimodal behavioral cues significantly improved predictive performance. Combining these cues with visual attributes provided complementary information, and the MFM outperformed simpler fusion methods by capturing complex interdependencies. These comprehensive evaluations across diverse scenarios demonstrate MUXAS-VR’s usefulness in predicting multiple UX dimensions.

In the past decade, there has been a significant increase in the growth of the global technology employment market, primarily owing to the digital age, artificial intelligence expansion, cloud computing, and cybersecurity requirements. Consequently, the demand for IT professionals has increased. Introductory programming is a crucial early course in IT higher education as it aids in developing problem-solving skills. However, such courses tend to result in substantial failure and attrition rates. Some studies have proposed solutions such as peer learning and personalized feedback, but challenges persist. One key assumption is that teacher support is likely to help mitigate these rates. However, with limited class time, teachers find it challenging to support all the students. This study aims to automatically assess the programming logic understanding level (PLUL) during introductory programming courses. We proposed a method for classifying PLUL through a block-coding learning platform based on mouse-tracking heatmaps using machine learning techniques. We collected actual learning-task data from first-year university students and compared the performance of five machine learning models. The decision tree algorithm achieved an accuracy score of 76.00%, F1 score of 77.36%, sensitivity score of 75.93%, and specificity score of 76.09%. These findings demonstrate that our method can effectively classify students based on their PLUL of tasks on a block coding learning platform.

Explainable AI (XAI) has been developed to make AI understandable to humans by providing explanations of its outputs. However, multiple types of AI explanations displayed on a screen could distort users’ trust in AI and their decision to rely on it. This could lead to poor task performance. In this study, we experimentally investigated the influence of AI explanations on trust and acceptance of AI results using a visual task. As a result, we found that participants increased their trust and acceptance of AI results with multiple types of explanations even though this did not improve task performance. These results, showing over-trust and over-reliance in human-agent interaction, were discussed along with cognitive load and cognitive bias caused by XAI.

Communication robots have the potential to contribute to effective human-XAI interaction as an interface that goes beyond textual or graphical explanations. However, it is not clear how we can develop an adaptive strategy to use a robot’s physical and vocal expressions depending on the context in dynamic interactions. This paper proposes a method for a communication robot to decide where to emphasize XAI-generated explanations with physical expressions. In the method, a user model predicts the effect of emphasizing certain points on a user and aims to minimize the expected difference between predicted user decisions and AI-suggested ones. We conducted a user study to investigate how emphasis selection with our method affects the performance of user decisions. The results suggest that our method guides a part of users to better decisions when the performance of the AI suggestion is high.