Cutting guides are widely used in cranio-maxillofacial surgery by providing mechanical references for precise bone resections and reducing intraoperative variability. Nevertheless, their rigid and patient-specific design requires dedicated CAD modeling and fabrication, making them time-consuming to produce and difficult to adapt when anatomical conditions or bone surfaces change. This work presents a hybrid surgical cutting guide that combines a physically adjustable device with Augmented Reality (AR) feedback to support intraoperative alignment in maxillofacial osteotomies. The concept merges the tactile reliability of conventional guides with the adaptability of digital visualization, enabling surgeons to fine-tune the cutting plane directly through AR tracking. Registration was performed using cephalometric landmarks and an inside-out tracking approach with HoloLens 2, allowing precise superimposition of virtual cutting planes onto 3D-printed mandibular models. The system was evaluated by both expert and novice operators under three feedback conditions: no AR, holographic overlay, and real-time distance guidance. Results showed that AR feedback was associated with improved positional accuracy, with mean linear deviations of 1.27±0.71mm and angular errors of 4.46±3.27°. Operator experience influenced overall performance, yet enhanced feedback compensated part of this variability. Combining physical and digital guidance can yield more adaptable, precise, and reusable osteotomy tools, paving the way for flexible surgical assistance in clinical settings.

Biofibre explorer: An augmented reality (AR) tool to promote circularity through material knowledge

AR Tunnel: An augmented-reality digital twin for immersive learning of wind tunnel laboratories

The use of augmented reality (AR) for pre-procedural planning has increased over the past decade, yet its real-time application during complex cardiac interventions remains novel. This case highlights the integration of AR technology during a challenging transcatheter pulmonary valve replacement (TPVR) using a self-expanding system in a pediatric patient. A 12-year-old male with a history of congenital pulmonary valve stenosis presented with progressive right ventricular dilation and exercise intolerance. Dynamic CTA was processed via ARTICOR® software (Artiness, Milano, Italy) and visualized through the Microsoft HoloLens for intraprocedural support. During initial balloon sizing, unexpected waist formation raised concern for wire malposition. AR visualization enabled identification of native leaflet avulsions/perforations previously unrecognized on conventional imaging. With this insight, successful wire repositioning and repeat balloon sizing were achieved, allowing for the accurate valve deployment. This case demonstrates the feasibility and clinical utility of AR-guided procedural support in complex TPVR. Real-time 3D visualization holds significant promise for improving safety and precision in congenital heart interventions.

Effective sorting is an indispensable component in construction waste material reusing or recycling. By harnessing both robots’ precision and humans’ adaptability, human-robot collaboration (HRC) is widely regarded as a promising approach for construction waste sorting (CWS). However, the lack of an intuitive interface hampers efficient communication between humans and robots. To address this gap, this paper proposes a user-friendly, hands-free interaction method for human-robot collaborative CWS. It does so by integrating a large language model (LLM) into an augmented reality (AR) head-mounted display, enabling human workers to communicate with robots using natural language while visualizing recognition and intentions in an augmented view. A prototype system was developed and tested in several CWS scenarios. It is discovered that the prototype can accurately interpret verbal instructions with 94.03% accuracy and achieve a sorting accuracy of 96.48%. The hybrid use of vision and vocal communication enables near-instant interaction, significantly enhancing the quality and efficiency of CWS. This research advances the field of cleaner materials by reporting a waste recycling and upcycling methodology integrating latest technologies such as HRC, AR, and LLM.

Technological advances in extended reality (XR: technology incorporating virtual reality (VR), augmented reality (AR), and mixed reality (MR)) have spurred the development of surgical simulators with the goal of immersing the user in the environment. However, there has been only incremental progress in developing surgical proficiency assessments that are critical in utilizing this technology to train safer and more efficient surgeons. We hypothesize that baseline measurements of expert surgeons (ground truth) and comparisons between users of different proficiency levels can be analyzed to develop an accurate proficiency classification of trainees on a VR-surgical simulator. The foundation of this work is based on our functional endoscopic sinus simulator (FESS) which incorporates a hierarchical task-based analysis of sinus surgery with motion-tracking of the endoscope and surgical instruments. We utilized dynamic time warping (DTW) to combine motion-tracking and operative time of sinus surgery experts to estimate a ground truth, developed a classification network combining motion-tracking and operative time using Decision Tree C5.0, and validated the accuracy of the proficiency classification by conducting extensive experiments with novice (n = 28) and expert (n = 24) users. The proposed work is critical to provide personalized and directed feedback to efficiently train surgeons utilizing simulation.

This narrative review explores evidence- and simulation-based teaching strategies for tracheostomy management (TTM) and dysphagia in adult patients, focusing on immersive technologies and instructional approaches within speech and language education. Increasing clinical complexity and multimorbidity demand advanced competencies, yet many high-risk skills cannot be practiced safely in real-world settings. Simulation-based learning environments, including extended reality (XR) technologies such as virtual reality (VR), augmented reality (AR), and mixed reality (MR), provide safe, repeatable, and scalable opportunities for clinical reasoning, psychomotor skill development, and interprofessional communication. Evidence shows that VR-based TTM training improves knowledge acquisition, skill retention, and confidence. Competency-oriented instructional frameworks, including 4C/ID and ADDIE, with structured feedback and debriefing, support staged skill development. However, standardization of clinical workflows, guidelines, and outcome measures remains limited, particularly in speech and language therapy-specific scenarios. XR-based teaching enhances clinical, communication, and interdisciplinary skills in TTM. To fully realize its potential, standardized clinical guidelines, workflows, and evidence-based teaching standards are needed. Integrating VR within structured instructional models ensures pedagogical rigor and safe transfer of learning to clinical practice.

Educational robotics and augmented reality for improving social skills of children with ADHD: A scoping review.

Intraoperative Analysis of Collateral Ligament Elongation Patterns by Coronal Plane Alignment of the Knee Classification Using Augmented Reality Navigation in Japanese Osteoarthritic Knees.

This study investigates the adoption of immersive technologies (ITs), including virtual, augmented, and mixed reality, in the hospitality and tourism sector. While traditional technology adoption models emphasize IT's relational factors, this research integrates experiential drivers, such as perceived immersion, enjoyment, and ease of interaction, and provides a comprehensive explanation of their motivational, psychological, experiential, and behavioral implications. A deductive, quantitative three-wave longitudinal survey design was employed. Data were collected from tourists visiting hotels, museums, heritage sites, and major tourist destinations at three-time intervals, enabling temporal separation of predictors, mediators, and outcomes. Findings reveal that immersive technology adoption significantly enhances perceived values (utilitarian, hedonic, and eudaimonic), which, in turn, foster subjective well-being (SWB) and brand evangelism (BE). Additionally, promotion-focused consumers exhibited stronger tendencies toward evangelism. This study contributes to the literature by, advancing technology adoption research through a theoretically integrated framework grounded in Service-Dominant Logic, Uses and Gratifications Theory, and Regulatory Focus Theory, tested using a longitudinal design in an emerging tourism context. Practically, it underscores the need for tourism marketers to create emotionally engaging experiences that not only enhance consumers' SWB but also cultivate long-term brand loyalty and turn them into evangelists.

Understanding thermodynamics concepts remains a major challenge in physics education due to their high level of abstraction and dependence on symbolic representation. Traditional instruction often fails to provide students with perceptual experiences that link theory to phenomena, thereby limiting the development of critical thinking skills. To address this issue, this study integrates Augmented Reality (AR) into thermodynamics instruction to explore its potential in enhancing students' critical thinking abilities. The research focuses on five key indicators—interpretation, analysis, evaluation, inference, and reflection—to examine how immersive visualization can transform students' conceptual understanding and reasoning processes. A mixed-method quasi-experimental design was employed, involving two matched groups: an experimental group learning through AR-based interactive simulations and a control group receiving conventional instruction. Quantitative data from pretest–posttest measures were analyzed using N-Gain, t-tests, and ANCOVA, while qualitative data were obtained through interviews, classroom observations, and document analysis. The findings reveal that AR significantly improved students’ critical thinking performance and fostered deeper conceptual engagement through visualization, experimentation, and reflective inquiry. The integration of AR functioned as a cognitive and epistemic mediator, bridging perception with reasoning and enabling students to construct scientific understanding through evidence-based reflection. These results affirm the potential of AR to transform science education into a more inquiry-driven and cognitively rich learning environment.

How does the connected AR-HUD warning system affect lane-changing strategies during freeway abandoned object events: A survival analysis approach.

Augmented reality for positioning embedded components in precast concrete: Device comparison and task performance

Real-time MRI-guided intervention (RTMGI) software with RGB-D–Based augmented reality navigation

Designing AI-driven augmented living spaces with spatial augmented reality to support the autonomy of individuals with amnestic mild cognitive impairment

Knowledgeable integration hybrid AI joins augmented reality and science literacy: Prospects and challenges

Focus on the experts: Co-designing an augmented reality eye-gaze tracking system with surgical trainees to improve endoscopic instruction

Bare-hand interactive progress observation for human-centric adaptive spatial augmented reality assembly

Registration between preoperative CT and intraoperative laparoscopic video plays a crucial role in augmented reality (AR) guidance for minimally invasive surgery. Learning-based methods have recently achieved registration errors comparable to optimization-based approaches while offering faster inference. However, many supervised methods produce coarse alignments that rely on additional optimization-based refinement, thereby increasing inference time. We present a discrete-action reinforcement learning (RL) framework that formulates CT-to-video registration as a sequential decision-making process. A shared feature encoder, warm-started from a supervised pose estimation network to provide stable geometric features and faster convergence, extracts representations from CT renderings and laparoscopic frames, while an RL policy head learns to choose rigid transformations along six degrees of freedom and to decide when to stop the iteration. Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 ± 8.18 mm, comparable to supervised approaches with optimization, while achieving faster convergence. The proposed RL-based formulation enables automated, efficient iterative registration without manually tuned step sizes or stopping criteria. This discrete framework provides a practical foundation for future continuous-action and deformable registration models in surgical AR applications.

Cross Reality (CR) is a new emerging field based on the current developments in Mixed Reality hardware, especially supported by the broad market penetration of video-based see-through Head-Mounted Displays. It refers to applications that span across different stages (real, Augmented Reality, Augmented Virtuality, Virtual Reality) of the reality-virtuality continuum, where users are interconnected between different stages and/or are able to transition between these stages. This publication follows the concept of other grand challenges publications and reflects the discussion of various researchers invested in CR. After an initial discussion at the 1st Joint Workshop on Cross Reality at IEEE ISMAR 2023, six topic groups have been identified, leading to 22 challenges, which were discussed in groups over the period of multiple months. The discussion of these challenges should act as a road map for future research in the area of CR.