As augmented reality (AR) adoption expands across training and operational domains, interaction design has served as a key determinant of user experience (UX). Although natural interaction techniques are believed to better support AR than traditional WIMP (windows, icons, menus, and pointing devices) interfaces, direct comparisons remain limited. This study compared natural and WIMP-based interaction paradigms in an individual, high-stakes procedural AR training task to evaluate their effects on usability, workload, presence, and task performance. Thirty participants completed a within-subjects experiment using both interaction paradigms in a Tactical Combat Casualty Care AR training scenario delivered on a Meta Quest 3. Condition order was counterbalanced. Subjective outcomes included the System Usability Scale, NASA Task Load Index (NASA-TLX), and Presence Questionnaire. Objective measures included task completion time, navigation errors, missed procedural steps, corrective actions, requests for clarification, and observed interaction smoothness. Paired-samples t -tests were used to compare conditions, with exploratory analyses examining order effects and relationships between immersive tendency and presence. Natural interaction produced significantly higher usability (p = 0.009) and presence (p = 0.001) than the WIMP interface. Participants also committed fewer navigation errors, missed fewer procedural steps, required fewer corrective actions, and demonstrated smoother interactions (p < 0.05) in the natural condition. No significant differences were observed for workload, task completion time, clarification requests, or observed efficiency. An order effect indicated lower perceived workload during participants’ second session, regardless of interaction condition, suggesting a learning effect. Natural interaction improves usability, presence, and interaction quality in individual, high-stakes procedural AR training while minimizing execution errors, although it does not necessarily improve speed or decrease cognitive workload during initial use. This evidence suggests that the principal benefits of natural interaction lie in improving the overall UX quality rather than maximizing efficiency, providing guidance for the design of future AR training systems.
Ryan J. Ward, Elias Griffith, Nadejda Krasteva, Matti Schwalk, Michael Batterley, Alan Marshall
Virtual reality (VR) environments rely on multisensory integration to create convincing perceptual experiences, but delivering many distinct odours is costly and technically challenging. We tested whether visual context is associated with context-dependent differences in evaluations of a compressed odour set in VR. Thirty participants viewed 20 distinct visual scenes while experiencing four odour blends representing floral, citrus, woody, and oriental; eight control scenes received randomly assigned blends or no odour. The family-congruent conditions reused the same physical odour blend across different visual contexts, and linear mixed-effects models (LMMs) assessed associations between the observed visual-odour conditions and the perceptual and physiological outcomes. The LMMs showed variation between visual stimulus and perceived visual-odour match, smell naturalness, and heart rate variability (HRV). No statistically significant associations were observed for perceived odour intensity, pleasantness, visual naturalness, or galvanic skin response (GSR). Odour family was not associated with statistically significant variation in the subjective ratings but was associated with HRV within the observed visual-odour pairings. Exploratory within-blend comparisons showed that these effects varied across blends and outcome measures. Stimulus-level Pearson correlations were then conducted as secondary, hypothesis-guided analyses to characterise whether the observed condition-level evaluation profiles covaried with perceived visual-odour match. Perceived visual-odour match was significantly associated with perceived intensity (r = 0.90), perceived pleasantness (r = − 0.78), and smell naturalness (r = 0.92), but not with visual naturalness, HRV or GSR. These correlations indicate that perceived match was associated with a broader perceptual profile rather than being an isolated semantic judgement of fit. This pattern may be consistent with a congruence-based account of crossmodal redirection, but we cannot prove that account. Overall, the findings indicate that the same physical odour blends were associated with selective, context-dependent differences in evaluation across visual scenes in VR.
Esther Putman, Kylie Haynie, Joi Harvey, Chad C. Tossell, Torin K. Clark, Allison P. A. Hayman
Adaptive-difficulty algorithms can automatically modulate task difficulty to improve skill acquisition of complex tasks. Using virtual reality (VR) enables training in an immersive way without requiring an in-person training facilitator or complex training facilities. However, it is not known whether an adaptive-difficulty algorithmic facilitation can outperform highly-motivated individuals facilitating their own training by self-selecting the difficulty level based on their own preferences. This study enrolled individuals (n = 45) with specialized aviation education to train using VR and then perform a spacecraft entry, descent, and landing task. Subjects were randomly assigned into one of three training groups: algorithm-facilitated condition, self-led condition, and static difficulty condition. Subjects trained for 30 trials (i.e., landings) in immersive VR, followed by 5 evaluation trials. Task performance, difficulty level, and crashes, as well as subjective reports of flow and motivation were collected. Despite training at a lower difficulty level, on average, than the self-led group (p = 0.0006) the algorithm-facilitated group had improved task performance compared to the static group (p = 0.004) and marginally significance compared to the self-led group (p = 0.097). Further, the self-led group reported lower confidence, as a subscale within flow (p = 0.048), and decreased flow late in training (p = 0.02) compared to the algorithm-facilitated group. The results of this study inform the utility and efficacy of algorithmic facilitation in learning complex tasks.
Gian Luca Dossena, Lukas Erle, André Helgert, Sabrina C. Eimler
The transition towards a circular economy (CE) requires more than technological innovation: it also depends on making complex circular systems understandable and translating their principles into practice. However, knowledge gaps and practical challenges continue to hinder the broader adoption of CE. Extended reality (XR) technologies may help bridge this gap by making abstract concepts, processes, and interdependencies tangible through interactive and immersive experiences. Beyond communicating CE principles, XR can also support their practical implementation by providing new ways to visualize information, simulate processes, and assist users in real-world tasks. Despite this potential, how XR is currently being applied within the context of CE remains fragmented and has not yet been comprehensively mapped. This paper addresses this gap through a PRISMA-guided scoping review of XR applications for CE, analyzing 24 publications across five major databases. We distinguish between public- and industry-oriented applications and examine their application domains, targeted CE strategies, and technological implementations. The results reveal distinct patterns in how XR is used across public and industry contexts, highlighting its potential to support circular practices while also exposing areas that remain largely unexplored. Together, these findings provide an overview of the emerging intersection between XR and CE and identify opportunities for future research and application.
Benedetta Del Lucchese, Elena Beani, Giada Martini, Veronica Barzacchi, Clara Bombonato, Gloria Mangani, Valentina Menici, Alessandra Bardoni, Rocco Salvatore Calabrò, Ilaria Maria Anna Cardillo, Valentina Di Giusto, Martina Favetta, Francesca Fumagalli, Carlotta Maria Manzia, Emanuela Pagliano, Stefano Parravicini, Francesca Rita Pulvirenti, Chiara Maria Tacchino, Giovanni Cioni, Giuseppina Sgandurra
