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As accuracy and performance increasingly dominate interactive systems, the temporal processes through which human understanding develops often recede into the background. Reasoning unfolds gradually through action, inspection, hesitation, and revision. In this paper, we investigate how embodied reasoning during physical calculation can be made perceptible by attending to how people act, look, and pause while interacting with a material artifact. We present a hybrid sensing prototype centered on a physical abacus that combines gaze, head movement, and bead manipulation to respond to moments of inspection as they emerge. Rather than displaying information continuously or inferring cognitive states, the system briefly surfaces numerical feedback when interaction patterns indicate checking or reflection. Findings suggest that usefulness depended less on what information was shown and more on when it appeared. We argue for interaction aligned with human sense-making rhythms, in which system responses follow observable reasoning activity without directing or accelerating it.
In this paper, we explore how extended reality (XR) and virtual reality (VR) can facilitate shared experiences that transcend individual boundaries. Using an example from the chemistry classroom, we illustrate how these technologies can enhance education and learning by fostering collaboration and shared understanding.Despite the vast potential of XR and VR, many of their applications remain isolated. Currently, sharing experiences often involves streaming onto a screen or verbal explanations, which can leave individuals feeling excluded if they are not directly involved. Even when multiple users wear VR or XR headsets, shared experiences are often limited to specific contexts like multiplayer VR games. Unfortunately, in domains such as education, work, or collaborative learning, these technologies are often underutilized.To bridge this gap, our study developed a prototype that leverages the eye-tracking and hand-tracking capabilities of the HoloLens2. This prototype enables collaborative interactions with 3D elements in an XR environment. By tracking participants' gaze within the XR scene, users can easily identify the element they are currently viewing. Through eye control and pinch gestures, users can manipulate digital content on the XR plane. When working within the same XR environment, users seamlessly share interactions and information, fostering a unified and collaborative experience with holograms.The current prototype is tailored to explore the intricate molecular details of sugar molecules within a collaborative XR environment. Teams of users can collectively delve into molecules such as fructose, glucose, galactose, and mannose, gaining deeper insights into their unique characteristics. Through interactions like eye-tracking and pinch gestures, users can directly interact with holographic 3D representations of these molecules. The prototype's user interface intuitively indicates which user is interacting with or observing each 3D object within the XR environment. Unlike conventional controllers, this prototype employs hand-tracking technology, enabling users to effortlessly navigate the XR interface using natural hand movements. This approach delivers an immersive and dynamic learning experience that surpasses traditional teaching methods, fostering heightened engagement and deeper understanding among learners. To assess the functionality and cognitive impact of the XR interface prototype, 19 participants were recruited for evaluation. Criteria for participant selection ensured they lacked prior knowledge or involvement in chemistry or related fields concerning sugar molecules. Following a brief introduction to the prototype's interaction patterns and eye calibration, participants engaged in testing across diverse learning scenarios. Initial survey findings suggest participants could swiftly distinguish between monosaccharides like glucose, fructose, galactose, and mannose based on their three-dimensional structures. However, results regarding gaze interaction evaluation are inconclusive and necessitate further experimentation.
In the context of design education, this study explores how a non-directive social robot (Furhat) can support early-stage idea generation. The robot was intentionally designed not to offer suggestions or solutions, distinguishing it from typical generative AI tools. Instead, it engaged students using metaphors, open-ended questions, and active listening. The aim was to examine how participants responded to a system that deliberately resisted the generative behavior commonly associated with tools like ChatGPT. Many participants appreciated the robot’s reflective role, noting that it helped them clarify and structure ideas without imposing direction. However, as the design process progressed, some expressed frustration and a desire for more proactive input—ranging from creative suggestions and emotional feedback to auditory elements, such as singing or whistling a melody. This mismatch between the robot’s expressive embodiment and its intentionally limited functionality led to moments of tension. Rather than viewing this as a shortcoming, we frame it as a deliberate design strategy: by withholding generative output, the robot fosters independent ideation. These findings contribute to broader discussions in human-robot interaction, particularly concerning creativity, user expectations, and the design of non-directive AI systems.
This paper introduces FocusMetrics XR, a system designed to enhance collaborative analysis in XR environments using three-dimensional gaze visualization. By aggregating users’ fixation data into real-time 3D heatmaps on virtual objects, the system highlights shared areas of attention, improving coordination and mutual understanding during collaborative tasks. FocusMetrics XR integrates the Mixed Reality Toolkit (MRTK) with Photon Unity Networking (PUN) to enable real-time synchronization, ensuring efficient performance in resource-constrained XR settings. A lightweight rendering algorithm is used to visualize fixation-based heatmaps on complex 3D geometries, while an interactive cube tool allows users to focus on specific areas, optimizing computational efficiency and reducing unnecessary tracking. The system is demonstrated through a medical use case involving a 3D heart model. Initial evaluations suggest that 3D heatmaps improve data interpretation and collaboration, leading to faster and more accurate task completion compared to traditional methods. Users particularly appreciated the gaze-sharing feature, which helped align focus during group activities. Applications extend beyond medical diagnostics to education, collaborative design, and environmental monitoring. Future work will explore scalability in larger teams and dynamic environments, supporting more complex multi-user interactions.
