Despite the growing sophistication of immersive virtual reality (VR) technologies, olfactory feedback remains underexplored in VR gaming. This study introduces Scent Ninja, a VR fruit-slicing game integrated with a custom head-mounted olfactory virtual reality display, and explores whether event-triggered fruit scents are associated with differences in presence, immersion, user experience, and gameplay performance. A total of 12 participants completed scent evaluation trials and gameplay sessions with and without scent. Results show that detection time, recognition, intensity, and pleasantness varied across stimuli, with Pineapple and Lime producing stronger perceptual responses. During gameplay, scents were associated with significantly higher transportation-related immersion ratings, while no significant differences were observed for presence, other immersion items, user experience, or gameplay performance. A significant order effect in perceived scent intensity suggests that sensitivity to olfactory cues may vary with task context and prior exposure within the gameplay environment. Qualitative feedback highlights the perceived importance of scent timing, odor fidelity, intensity, and alignment with in-game actions. The findings provide exploratory evidence that event-triggered olfactory cues may support the transportation dimension of immersion during active VR gameplay, while emphasizing timing, scent intensity, perceptual clarity, and contextual congruence as important design considerations for olfactory VR design.
Medimon is an educational role-playing game that integrates visual mnemonics, collectible creatures, disease states, treatment items, and artificial intelligence-powered non-player characters (AI-NPCs) to teach biomedical concepts. This single-arm mixed-methods feasibility pilot examined recruitment, gameplay uptake and progression, player experience, descriptive knowledge outcomes, and AI-NPC interactions during the Medimon endocrine level among high school students. Thirty students consented; 10 generated gameplay data, six completed paired 12-item pretests and posttests, and eight completed a PXI-style survey. Students played independently using a Steam-based build and encountered thyroid, pancreas, and adrenal educational content through exploration, quests, battles, and Medimon collection. Mean paired knowledge scores increased from 19.4% at pretest to 44.4% at posttest, although the change was not statistically significant in this small exploratory sample (p = 0.156). Audiovisual appeal, curiosity, and enjoyment were rated favorably, whereas Progress Feedback was the lowest-rated player-experience domain. Students who completed the posttest demonstrated greater gameplay duration, exploration, quest completion, and Medimon exposure than non-completers. Thematic analysis identified a mismatch between player expectations and AI-NPC scope, including fabricated quests, spatial directions, and game mechanics that introduced unreliable guidance into the educational environment. The study identified barriers related to gameplay uptake, progression feedback, posttest completion, and AI-NPC reliability that should be addressed before the educational efficacy of Medimon is evaluated in a larger controlled study.
In the maritime industry, it is common for crew members to be unfamiliar with their workplace until they are on board. This limits evacuation time, which is critical in emergency situations such as fire or shipwreck. The aim of this work is therefore to enhance the preparedness and safety of new crew members by enabling them to become familiar with the location of escape routes before boarding the ship. For this purpose, the use of 360 technology is proposed. A 360 tour is a virtual, interactive experience that allows users to explore a location as if they were physically there. On a ship, this allows the crew to know the escape routes and make quicker and safer decisions on the most appropriate route. This paper shows how a 360 tour was developed and tested on a merchant vessel. The evacuation time from the engine room has been measured for a group of people. The results showed that the 360 tour had a very positive impact on the evacuation time for these people who had not previously been physically present in the area.
The development of digital learning technologies has introduced innovative tools to enhance science and chemistry education, including PhET simulations. This study aims to evaluate the effectiveness of PhET simulations on students’ learning outcomes through a systematic literature review following the PRISMA 2020 guidelines. A systematic search of Scopus and Crossref databases was conducted (last search: January 2026) using predefined keywords. Eligible studies were empirical research published between 2020 and 2026 that investigated PhET simulations in science-related education and reported learning outcomes, while non-empirical studies and non-Scopus-indexed articles were excluded. Risk of bias was assessed using an adapted Joanna Briggs Institute critical appraisal tool. Due to heterogeneity in study designs and outcome measures, the results were synthesized using a narrative approach. A total of 14 studies across elementary to higher education levels were included. The findings indicate that PhET simulations consistently improve learning outcomes, particularly academic achievement and conceptual understanding, with effects generally favoring simulation-based instruction over traditional methods. However, higher-order skills and affective outcomes such as motivation and attitude remain less frequently investigated. The evidence is limited by variability in study designs, incomplete reporting of non-cognitive outcomes, and the absence of quantitative synthesis. Overall, PhET simulations demonstrate strong potential as an effective interactive learning medium, although their impact depends on instructional design, teacher facilitation, and technological accessibility.
The impact of oncological diseases extends far beyond the clinical patient, profoundly affecting the mental health of caregivers, family members, and volunteers who navigate complex emotional landscapes of grief, anxiety, and trauma. While the domain of digital health has seen a proliferation of serious games aimed at pediatric patient education and treatment adherence, the specific perspective of the “second-order patient”, the caregiver or survivor, remains significantly under-explored. The primary objective of this study is to systematically review the current state of interactive narratives in oncology, palliative care, and grief support, identifying research gaps to inform the broader design space of empathy-driven serious games. Following the PRISMA guidelines, 31 articles were selected from an initial query of 116 records. Interventions were categorized into Serious Games, Games, and Gamification. The analysis reveals a critical thematic transition: early interventions relied heavily on biological “battle” metaphors to empower patients, whereas the current literature advocates for “thanatosensitive” designs that foster empathy. However, a distinct research gap persists regarding narratives that explore post-loss meaning reconstruction and the hospital volunteer experience. Synthesizing these findings, this paper establishes an evidence-based theoretical framework demonstrating a significant opportunity for games that prioritize dialogue and emotional processing over traditional winning conditions. As a practical application of these findings, we also briefly outline the conceptualization of a prototype simulating a widower’s experience volunteering in a palliative ward, shifting the ludic focus from defeating a disease to navigating loss.
Virtual reality (VR) has increasingly been explored to support mental health interventions, including those informed by Cognitive Behavioural Therapy (CBT). However, many VR-based CBT systems report limited usability evaluation and minimal practitioner involvement, raising questions about their applicability beyond clinical settings. This study aimed to conduct a usability-focused validation of a gamified VR platform translating selected CBT-informed techniques for non-clinical use. Selected CBT techniques were implemented as short VR mini-games guided by a design rationale emphasising simplicity, symbolic interaction, and ease of use. A user study involving 58 university students evaluated usability using the System Usability Scale (SUS), ISO 9241-11-informed usability categories, and brief anxiety-related self-report measures. In addition, three psychological practitioners reviewed the platform and assessed therapeutic coherence and design alignment. Findings demonstrated good overall usability, positive immediate experiential responses, and practitioner support for the platform’s therapeutic coherence and suitability as a supportive digital wellbeing tool. The study demonstrates a structured usability-based validation approach for VR systems translating CBT-informed techniques and offers practical guidance for the design and evaluation of immersive mental health technologies.
Teacher adoption of virtual reality (VR) in education appears constrained despite the technology’s potential as an immersive multimodal learning environment. This cross-sectional online survey, based on convenience and snowball sampling, examined adoption perceptions among 408 Romanian teachers from primary, secondary, and tertiary levels using an integrated Technology Acceptance Model and Theory of Planned Behavior framework. Seven constructs were measured on five-point Likert scales and analyzed through internal consistency indices, confirmatory factor analysis, HTMT discriminant validity assessment, structural equation modeling, Spearman correlations, nonparametric group comparisons, supplementary manifest-score regression, and descriptive thematic coding of open-ended responses. The seven-factor CFA model showed acceptable fit (CFI = 0.945, TLI = 0.936, RMSEA = 0.068), and composite reliability and AVE supported convergent validity across all constructs. However, HTMT indicated limited discriminant validity between attitude toward using VR and attitude toward the behavior of adopting VR (HTMT = 0.928). In the SEM model, perceived usefulness showed the largest standardized association with attitude toward using VR, while behavioral intention was mainly associated with attitudinal evaluations and subjective norm; perceived behavioral control showed a weaker standardized path. All scales showed acceptable internal consistency (Cronbach’s α=0.81–0.95), and construct means exceeded the scale midpoint (range: 3.45–4.03), indicating generally positive but differentiated perceptions. Supplementary manifest-score regression was consistent with the SEM results: the unified attitude factor showed the strongest statistical association with behavioral intention, followed by subjective norm and perceived behavioral control. Descriptive thematic coding of open-ended responses identified training, infrastructure, equipment access, curriculum-aligned content, cost, technical support, and time as recurrent perceived conditions associated with self-reported VR adoption intentions. The findings suggest that educational VR adoption should be interpreted through self-reported human factors and perceived implementation conditions, including perceived control, access to immersive equipment, practical training, and institutional support.
Virtual reality (VR) has shown promising potential for upper-extremity rehabilitation; however, its successful integration into clinical practice depends not only on therapeutic effectiveness but also on the acceptance of the technology by patients and healthcare professionals alike. Despite growing international research in this area, there is limited evidence on clinical attitudes toward VR rehabilitation in Thailand and other middle-income settings. This study investigates Thai patients’ and clinicians’ perceptions of VR for upper-extremity rehabilitation through two complementary studies focusing on perceived usability and usefulness. The first study evaluated the perceived usability of a VR rehabilitation game using the System Usability Scale (SUS) among 40 first-time VR users divided into younger and senior groups. The younger group reported a higher average SUS score (64.6) than the senior group (55.4). While both groups generally perceived VR rehabilitation positively, senior participants expressed greater concern regarding system complexity, consistency, and the need for technical assistance. Nevertheless, the findings indicate that VR remained an acceptable rehabilitation approach even among elderly first-time users in a population with relatively lower technological readiness. The second study explored clinicians’ perceptions of utilizing VR-generated movement data to support rehabilitation decision-making. Five rehabilitation professionals evaluated the potential usefulness of VR data visualizations for diagnosis and treatment monitoring. Clinicians generally perceived VR data as valuable, particularly for tracking rehabilitation progress rather than diagnostic decision-making. Feedback from interviews also highlighted practical considerations for future implementation, including the importance of normative data, simplified visualization formats, and the feasibility of clinical workflows. By combining patient usability perspectives with clinicians’ evaluations of clinical usefulness, this research provides a broader understanding of the factors influencing VR adoption for upper-extremity rehabilitation in Thailand. The findings contribute contextual evidence from an underrepresented healthcare environment and offer insights relevant to the future deployment of VR-assisted rehabilitation systems in similar socio-economic settings.
Computational thinking (CT) is increasingly recognized as essential in education, yet teacher preparation programs struggle to develop both computational proficiency and pedagogical readiness in pre-service teachers (PSTs). This study examines an AI-mediated, game-making course grounded in the emerging “vibe coding” paradigm, where 24 novice PSTs iteratively constructed programs through natural language prompting. Adopting a mixed-methods design, the study drew on pre- and post-course attitude questionnaires, reflective accounts of prompting strategies, and open-ended responses. Results indicate that participants substantively engaged with core CT practices, particularly debugging, iterative refinement, and problem decomposition. Nonetheless, this downward recalibration in self-reported coding and teaching confidence represents a productive adjustment rather than a failure. Conversely, attitudes toward game-making improved significantly, with a statistically significant medium effect size for perceived instructional value (d = 0.51), the largest practical effect observed across dimensions. Most participants intended to integrate CT into future teaching. These findings suggest that prompt-driven learning environments support meaningful engagement with computational processes when carefully scaffolded, but do not inherently ensure pedagogical readiness, particularly for higher-order CT practices such as abstraction and pattern recognition. Unlike prior research that has examined game-making processes or PST attitudes toward CT in isolation, this study empirically integrates all three within a single scaffolded instructional design using vibe coding. This integration enables a process-level account of how CT is enacted—and how it develops—when code generation is partially delegated to AI systems. Beyond documenting attitude shifts, the study introduces an analytical rubric for identifying CT engagement in AI-mediated prompting and derives evidence-based design principles that specify the pedagogical conditions under which vibe coding supports, rather than bypasses, computational reasoning.
Universal Accessibility in Astronomy requires a paradigm shift from visual-centric communication to multisensory data interaction. Because astronomy communication relies inherently on high-resolution imagery and visual metaphors, it creates significant accessibility barriers for blind and low-vision (BLV) audiences. To address this, multimodal encoding offers a feasible and meaningful solution by redistributing information across alternative sensory channels, ensuring that the absence of sight does not preclude the comprehension of spatial data. This article explores the development and evaluation of a low-cost, multimodal tool designed to represent complex astronomical concepts—specifically stellar magnitude and color—through tactile and auditory stimuli. Unlike traditional methods, our approach focuses on the haptic-cognitive link, allowing users to “feel” data through physical relief models. We present a structured impact study involving a heterogeneous group of blind, low-vision, and sighted participants. The methodology followed a mixed-methods approach, including a participatory workshop with 20 individuals and a detailed usability assessment with a core group (n= 6) of blind and low-vision participants. Preliminary results from this pilot phase demonstrate that multimodal integration effectively reduces the perceived mental effort for complex spatial data comprehension. Quantitative and qualitative feedback suggests that tactile-auditory sensory substitution not only improves accessibility but also enhances engagement and information retention across all user groups. These findings highlight the potential of multimodal models in transforming public scientific environments, such as museums and observatories, into inclusive, interactive spaces.
Non-alcoholic fatty liver disease affects approximately thirty percent of the global population, yet public awareness remains dangerously low among young adults facing occupational risk factors. This study introduces the Fatty Liver Awareness Game (FLAG), an educational serious game designed to improve liver health literacy among software engineering students at the University of Guayaquil. While evaluated with this specific sample, FLAG is intended for the broader target population of young adults in developing nations who face occupational sedentary risk and limited access to preventive health education. Through a controlled experiment with fifty participants randomly assigned to game-based or traditional lecture instruction, the game demonstrated superior effectiveness, with a twenty-percentage-point advantage in post-test scores and a seventy-two percent reduction in incorrect responses compared to fifty percent in the lecture group. The large effect size (Cohen’s d = 1.43) and reduced performance variability among game participants indicate that interactive, feedback-rich learning environments can outperform passive instruction for this population and content domain. While the present design does not isolate the contribution of individual game elements—such as narrative framing, explanatory feedback, or mini-game interleaving—the results establish FLAG as a replicable model for digital health interventions targeting underserved populations at critical developmental junctures. Future component analyses are needed to determine which specific design features drive the observed advantages.
This study examines how AI-enhanced motion capture (AI-MoCap) mediates the preservation, transmission, and re-creation of Chinese shadow puppetry as performative intangible cultural heritage. Through a state-of-the-art review and comparative analysis of three representative application models-technology-driven, culturally integrated, and entertainment-oriented-the paper explores how AI-MoCap supports the digitization of performative techniques while reshaping modes of cultural presentation and interaction. Cross-case comparison highlights recurring tensions between technical standardization and cultural authenticity while also indicating possibilities for symbolic reconstruction, contextual continuity, and ethically grounded design. Based on this comparison, the paper develops a dual-channel inheritance framework-"perception-symbol" and "design-performance"-and treats cultural resolution and digital ethics as analytical and normative principles for resisting algorithmic homogenization. Rather than functioning only as a digitization tool, AI-MoCap can be understood as a mediating mechanism whose cultural value depends on how it remains embedded in community-based performative logics, symbolic systems, and ethical boundaries. The resulting framework offers transferable guidance for future research, curation, training, and policy discussion in the digital safeguarding of performance-based heritage.
Trajectory planning algorithms are essential in human-robot collaboration (HRC), as they must generate efficient trajectories for seamless interaction. Given the risks and complexity of testing in real-world scenarios, a virtual environment was developed in Unity 3D, integrating a virtual model of the UR3 robot that delivers workpieces to a user equipped with a Meta Quest device. The RRT, RRT-Star (RRTS), and RRT-Connect (RRTC) algorithms were evaluated using ANOVA and Tukey post hoc tests, considering the following response variables: safety, feasibility, smoothness, and computation time across three experimental scenarios characterized by (i) low, (ii) medium, and (iii) high levels of movement of the participant's left hand. The statistical results indicate that RRTC exhibited the best performance in terms of smoothness and computation time. Based on these findings, a multicriteria decision-making analysis was conducted using the Analytic Hierarchy Process (AHP), combining quantitative evidence derived from the statistical analysis with expert judgments supported by bibliographic references. This multicriteria analysis enabled the coherent integration of the different evaluation criteria and concluded that RRTC is the most suitable alternative for collaborative assembly tasks in HRC environments.
Augmented Reality (AR) has been found to produce significant effects on individual learning outcomes but its impact on collaborative applications remains moderate. Existing AR frameworks emphasize individual instructional design, whereas frameworks for collaborative learning rarely engage with the spatial and device-mediated affordances of mobile AR. In response to this inadequacy in the literature, we introduce the Mobile Augmented-Reality Storytelling for Vocational Education and Training (MARS-VET) framework, a four-dimensional conceptual architecture that integrates Computer-Supported Collaborative Learning (CSCL) scripting principles with mobile AR affordances for collaborative English as a Foreign Language (EFL) writing in Vocational Education and Training (VET) settings. MARS-VET synthesizes theoretical perspectives across four dimensions: contextual anchoring, which embeds activities within authentic workplace scenarios; collaborative orchestration, which structures group interaction through macro- and micro-level scripts; competency cultivation, which sequences writing progression from model-based reproduction toward autonomous professional text production; and capacity building, which addresses the professional-development requirements of implementing educators. Content validity was established through expert panel evaluation involving international specialists (N = 11) who rated the framework against 36 items using a four-point relevance scale and provided additional qualitative feedback. The Scale-level Content Validity Index (S-CVI/Ave = 0.91) exceeded established thresholds, with all four dimensions achieving satisfactory item-level indices. Experts reached unanimous agreement on items addressing workplace scenario identification and co-located access to linguistic resources. Qualitative feedback led to terminology refinements and clarification of orchestration mechanisms. The framework offers VET institutions and educators a reference for the design and evaluation of collaborative AR experiences in an area where integrative frameworks have so far been lacking.
Despite significant advances, multimodal sentiment analysis still faces critical challenges in modeling complex cross-modal interactions and extracting discriminative sentiment features. To address these limitations, this paper proposes a hierarchical multimodal sentiment analysis framework. Specifically, a cross-modal feature enhancement module is first introduced to capture deep correlations among textual, visual, and acoustic modalities via cross-attention mechanisms, thereby obtaining context-aware fused representations. Subsequently, an attention-gated feature disentanglement approach is employed to effectively separate sentiment-relevant information from content-specific features within the fused representations; an independence loss is further imposed to enforce orthogonality between these two feature subsets, thereby mitigating noise induced by repetitive visual frames and textual stop words. Finally, all disentangled features are integrated to facilitate high-level sentiment reasoning through a multimodal logical inference module, where supervised contrastive loss is incorporated to enhance the discriminability of sentiment expressions. Extensive experiments conducted on two public benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that the proposed framework achieves improvements of 2-6% across multiple evaluation metrics compared with state-of-the-art methods.
The Camino de Santiago, a UNESCO-listed pilgrimage route, has experienced sustained growth in visitor numbers, challenging municipalities to preserve cultural integrity while ensuring service quality. This study reviews people-counting technologies and proposes a smart pilgrim management framework grounded in flux measurement systems to support data-driven and sustainable decision-making. Drawing on the smart tourism literature, the conceptual framework integrates infrared counters, mobile tracking solutions, and GPS/Wi-Fi data to generate real-time insights into pilgrim flows. A pilot simulation illustrates how these data can inform operational and strategic planning. The framework enables local authorities to monitor pedestrian movements, anticipate service demands (sanitation, accommodation, and safety), and detect overcrowding in sensitive heritage areas. By incorporating technological solutions into traditionally low-tech pilgrimage settings, municipalities can transition from reactive to proactive management approaches. The paper contributes a scalable and ethically grounded framework tailored to heritage pilgrimage routes, advancing smart tourism applications in culturally significant contexts.
Blended emotion recognition introduces the challenge of identifying not only which emotions are present in an expressive display but also their relative salience. The proposed methodology builds upon the pre-extracted features provided with the dataset and enhances performance through a combination of temporal modeling and multimodal fusion strategies. Unimodal experiments revealed that visual encoders consistently outperformed audio ones, with the multimodal HiCMAE encoder achieving the strongest single-encoder results with 34% presence accuracy and 18.23% salience accuracy. Multimodal fusion further improved performance, with the best validation results obtained using a combination of simple concatenation and attention-based fusion, reaching 47.86% in presence accuracy and 27.92% in salience accuracy. Overall, the proposed methodology surpasses the chosen baseline introduced in the original paper across a k-fold experiment, confirming the effectiveness of multimodal attention-based fusion for the accurate prediction of both emotion presence and salience in blended affective behaviour. The experimental results further indicate that multimodal expression recognition consistently outperforms unimodal approaches, highlighting the complementary nature of cross-modal information.
In recent years, artificial intelligence has been fully involved in design practice and educational activities, and its impact on practice and education has received widespread attention from the academic community. This study aimed to preliminarily explore, through a controlled experiment, the differences in the impact of generative artificial intelligence (AI) tools and traditional web/literature tools on the sustainable design learning outcomes of interior design students in a specific teaching context at a university in China. A study was conducted on 58 third-year college students who were divided into an AI tool group (Class B) and a traditional tool group (Class A). Three semi-structured questionnaire surveys were conducted over two months to collect data on their understanding, attitudes, and practical applications of sustainable design. Quantitative statistics and text analysis methods were used for the comparison. The results showed that under specific experimental conditions, students who used AI tools showed a more significant improvement in their self-evaluation of knowledge mastery, but their sense of recognition of the importance of knowledge and subsequent learning willingness also decreased. In subsequent design practice, students in the traditional tool group showed higher initiative in applying concepts and diversity in strategies. Text analysis further suggests that AI-assisted learning may be more conducive to the rapid structured acquisition of knowledge, while traditional learning methods exhibit different characteristics in promoting deep semantic associations. The conclusions of this study are based on short-term experimental observations of specific samples and toolsets, revealing the tension between efficiency and depth that may be faced when integrating AI tools into interior design education, providing a reference and discussion basis for broader and longer-term teaching research in the future.
Fitts' law is a foundational model for predicting pointing performance and has been increasingly explored in immersive virtual reality (VR) environments. This paper presents a controlled experimental framework for deriving modality-specific Fitts' law models in VR and evaluating their predictive transfer to applied interaction tasks. The framework comprises two scenarios. The first replicates a standardized ISO 9241 pointing task in a 3D virtual environment to derive predictive movement time models by systematically varying target distance (20-50 cm), target size (2.5-5 cm), and spatial configuration (0 degrees, 45 degrees, 90 degrees, 135 degrees). The second simulates an applied warehouse-inspired task involving tool sorting and structured placement actions to evaluate the generalizability of the derived models in more ecologically valid VR interactions. Thirty-two participants completed all tasks using the Meta Quest 3 headset and two interaction modalities: a handheld controller and hand tracking with gesture recognition. Results show that Fitts' law remains a strong predictor of movement time for 3D pointing in VR, with high linear fits for both the controller (R2=0.9615) and hand tracking (R2=0.9668). However, models derived from standardized pointing tasks showed limited transferability to applied object-manipulation scenarios, producing prediction errors of approximately 27-35% and systematically underestimating movement times. Additionally, both objective metrics and subjective evaluations indicated that controller-based interaction outperformed hand tracking in efficiency, accuracy, perceived workload, and usability. These findings highlight both the robustness and limitations of Fitts-based performance modeling in realistic VR interaction contexts.
This study presents a multimodal deep learning framework for automatic proficiency and style classification of parallel Bilingual Tamil-Hindi learner data. The proposed system employs a dual-headed neural architecture to simultaneously predict proficiency levels (Basic, Advanced) and stylistic categories (Formal, Literary) using shared feature representations. A curated dataset of bilingual text samples is utilized, along with synthetic speech generated through text-to-speech (TTS) to enable controlled multimodal experimentation. Five deep learning architectures are evaluated under text-only, audio-only, and learnable fusion settings. Experimental findings indicate that text-based models consistently achieve strong performance in both proficiency and style classification tasks. In contrast, the audio-only model demonstrates limited effectiveness, highlighting the constraints of synthetic acoustic features in capturing meaningful linguistic information. The fusion models provide only marginal improvements over text-based approaches, suggesting that textual representations play a dominant role in proficiency and stylistic classification within controlled datasets. These results emphasize the importance of linguistic features over acoustic signals for automated language assessment in low-resource settings. The proposed framework provides a scalable and reproducible approach and offers a foundation for future work incorporating real speech data and more diverse linguistic inputs.
