Students with learning disabilities often experience challenges in understanding scientific concepts and acquiring related skills. Augmented reality, as a technology-based instructional tool, has significant potential to improve science learning for these students. This study examines the effectiveness of an augmented reality-based intervention in teaching the muscular and skeletal systems to students with learning disabilities. The dependent variable was the percentage of correct responses to questions related to these systems, while the independent variable was the augmented reality-based intervention. The study included three students with learning disabilities and utilized a multiple probe across participants design. The findings indicated that students successfully learned the muscular and skeletal systems, maintained their learning over time, and demonstrated successful generalization. Social validity data indicated that both students and teachers had positive perceptions of the intervention. The study provides evidence supporting the effectiveness of AR-based intervention in science education for students with learning disabilities.

Whether “looking alike” implies “judging alike” remains an open question. The present study examined the relationship between gaze consistency and aesthetic consistency during abstract art viewing. Participants viewed abstract images and provided emotionaland aesthetic ratings across multiple dimensions. Gaze consistency was measured using complementary metrics of spatial overlap, sequential similarity, and fixation dispersion. We tested whether emotional appraisal modulates both aesthetic and gaze consistency, whether the two consistencies are associated, and whether component-level consistencies predict overall aesthetic consistency. Results showed that emotional appraisal significantly predicted aesthetic consistency, whereas gaze consistency was primarily associated with low-level structural properties of the stimuli. Critically, gaze consistency and aesthetic consistency did not reliably predict one another, revealing a dissociation between them. Overall aesthetic consistency was best explained by component-level consistencies, especially color consistency. These findings refine theories of aesthetic judgment and provide guidance for perceptual–emotional system design in human–computer interaction.

In esports, competitive performance is directly reflected in players’ in-game behavior. However, the interrelationships among in-game behavior and performance still remain inadequately understood. First-person shooter (FPS) games provide an important context for studying the organizational structure and interactions of in-game behavior in esports. This study employs network analysis to explore 39 in-game behavioral indicators among professional Counter-Strike players across 1,468 players from CS:GO (2012–2023) and 880 players from CS:2 (2023–2025). The research objective is to construct and explore the network structure, invariance, and community characteristics of in-game behavioral networks. Results showed that stronger associations primarily occurred within the same in-game behavioral dimensions, while some cross-dimensional connections were also observed. Certain core in-game behaviors maintained a high level of node strength and expected influence across both periods, particularly metrics related to the Entrying, Trading, and Firepower dimensions. The rankings of the relative importance of bridging strength and predictability for in-game behaviors varied between CS:GO and CS2. Network Comparison Test (NCT) results revealed significant differences between the two periods in both global network strength and network structure. Stability tests confirmed the reliability of the network analysis. Additionally, the behavioral dimensions identified through community detection differ from the original dimensions. This study provides empirical evidence for understanding the interrelationships among in-game behaviors in esports. It offers a potential analytical perspective for enhancing esports athletes’ performance, training feedback, and tactical management, while also advancing interdisciplinary development in human-computer interaction and esports.

As modern systems grow increasingly complex and expansive, the industry faces the critical need to adopt more efficient engineering approaches. Model-Based Systems Engineering (MBSE) has emerged as a prominent approach to streamline the development of complex systems. Nonetheless, the adoption of MBSE has often been hindered by the complexity of abstract modelling languages and the difficulty of managing dispersed information across multiple tools. To address these barriers, recent studies have explored integrating virtual reality (VR) into MBSE workflows to provide a comprehensive, unified environment that consolidates all necessary information and enables more intuitive interactions. This paper proposes a novel immersive VR interface to improve clarity through enhanced visual notations and more accessible interactions. The current study presents various validation methods and demonstrates promising outcomes compared to traditional MBSE tools, particularly in user satisfaction, visual notation, and cognitive load, highlighting the potential of immersive VR environments to transform systems engineering practices.

Threads, a text-based social media platform launched by Meta, grew rapidly but soon saw declining engagement.Guided by uses and gratifications theory, this study examined how gratifications (content presentation, agency, interactivity, and navigability)relate to engagement depth and continuance. Using PLS-SEM and fsQCAwith 622 Threads users,we found that low-engagement behaviors (e.g., browsing, liking) are positively associated with continuance, whereas high-engagement behaviors (e.g., posting, commenting) may weaken loyalty. Moderation analysis revealed that the number of social media platforms used weakens this behavioral effect; users with more than five platform accounts are less likely to translate their engagement into continued use. This finding suggests that in the context of poly-social-media, time budgets significantly shape loyalty. Furthermore, fsQCA identified multiple pathways of gratifications and behaviors that lead to continuance. This study therefore extends U&G by connecting gratifications to engagement depth and configurational outcomes and offers implications for sustaining retention amid multi-platform migration.

As smart cabins become more prevalent, in-vehicle displays can enhance the occupants’ experience but may also increase the risk of motion sickness (MS) by intensifying sensory conflict. While peripheral visual motion cues, particularly those related to road visibility, are known to affect MS, the role of display location remains unclear. Thus, this within-subject on-road experiment investigated how display locations (including roof, backrest, and armrest displays) and view conditions (unblocked view with road environment versus blocked view) influence MS. Results showed that the external-view condition was associated with lower MS than the view-blocked condition, confirming the importance of peripheral motion cues. Across both view conditions, roof display was associated with the lowest MS. Under unblocked views, this advantage likely stemmed from improved peripheral visual information, situational awareness, and motion anticipation. Under unblocked views, roof display may still reduce MS through more favorable head posture and motion patterns. These findings highlights display placements as critical HMI design factor for mitigating MS in smart cabin.

This study presents a portable and cost-effective brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) to enable non-verbal communication for patients in intensive care units (ICUs). A custom wireless single-channel electroencephalography (EEG) acquisition system was developed to transmit the acquired EEG data via Wi-Fi using an ESP32 microcontroller to a Raspberry Pi processor. The participants interacted with a screen that displayed multiple flickering icons, each corresponding to a predefined message. EEG data were acquired while subjects focused on a target icon, generating a unique SSVEP response. Canonical correlation analysis (CCA), incorporating harmonic frequencies, was used for real-time classification. Twelve healthy participants were involved in the study and the proposed system demonstrated strong performance, yielding a mean accuracy of 98.33% for classification tasks and achieving an average information transfer rate (ITR) of 40.98 bits/min. Detection latency was averaged 3.215 s, with a computation time of 215 ms. Upon classification, an audio message was generated and simultaneously sent to caregivers via the short message service (SMS) and the multimedia messaging service (MMS). This system demonstrates a reliable and efficient single-channel SSVEP-based BCI, integrating real-time processing, wireless data transmission, and multimodal feedback. Its portable design and high accuracy make it highly suitable for practical deployment in ICU settings, providing an effective communication alternative for critically ill, non-verbal patients.

The rapid growth of information technology and social media has increased online scams targeting older adults. Existing interactive digital interventions remain limited, as older adults often struggle to understand complex scams and respond to evolving scenarios. To address this, we developed a generative AI-based role-reversal application in which the AI simulates a scam victim, while older adults act as persuaders to guide the AI in recognizing and avoiding fraud. A total of 40 older adults were recruited and assigned to either the experimental group (n = 20) or the control group (n = 20). Results showed positive user experience and improved self-efficacy in AI interaction. The experimental group reported significantly higher attractiveness, stimulation, and novelty. Significant group × age interactions were found for dependability and perspicuity, while group × education interactions affected stimulation and perspicuity, indicating moderating effects of demographic factors. These findings inform design guidelines for GAI-based fraud prevention and highlight future directions.

In today’s mobile-connected world, phubbing has become a common form of social neglect. This research examines how perceived phubbing shapes attitudes toward artificial intelligence (AI), especially anthropomorphic service robots. Drawing on compensatory control theory, we propose that perceived phubbing increases social anxiety and need for control, which in turn foster more favorable attitudes toward AI. Study 1, a recall-based experiment, shows that perceived phubbing increases acceptance and trust toward AI through these two psychological mechanisms. Study 2 uses a video-based design to test AI anthropomorphism. Under perceived phubbing, robots with moderate human-likeness tend to elicit more favorable responses than robots with lower or higher human-likeness, with the clearest evidence for willingness to accept. The findings show how negative social experiences shape human-AI interaction and offer implications for the design of customer-facing service robots.

This study explores how integrating biophilic elements into automated vehicles affects users’ experience, affective trust, perceived price value, connectedness to nature, and behavioral intention. Sixty-one participants experienced 4 driving scenarios with three types of biophilic interventions in a mixed reality environment. Results from Partial Least Squares revealed that perceived price value was the strongest predictor of behavioral intention, while final trust and connectedness to nature showed no direct impact. Initial trust influenced behavioral intention indirectly by enhancing user experience, which increased perceived price value. Demographic factors, did not significantly affect outcomes. These findings show the value of integrating biophilic elements in automated vehicles lies not only in making the ride more pleasant, but in strengthening the factors that drive adoption. This research provides pragmatic insights for automotive designers and industries. In a market where technology frequently feels impersonal, integrating natural elements can transform the driving experience, making it not only more pleasant but also more desirable to potential customers. HIGHLIGHTS Biophilic elements in AVs enhance user experience and behavioral intention. Perceived price value is the strongest predictor of behavioral intention. Initial trust boosts behavioral intention indirectly via improved user experience.