The interior design suffers from inefficiency and a lack of aesthetic appeal. With the development of artificial intelligence diffusion models, using text descriptions to generate aesthetically pleasing designs has emerged as a new approach to address these issues. In this study, we propose a novel method based on the aesthetic diffusion model, which can quickly generate visually appealing interior design based on input text descriptions while allowing for the specification of decorative styles and spatial functions. The method proposed in this study creates creative designs and drawings by computer instead of from designers, thus improving the design efficiency and aesthetic appeal. We demonstrate the potential of this approach in the field of interior design through our research. The results indicate that: (1) The method efficiently provides designers with aesthetically pleasing interior design solutions; (2) By modifying the text descriptions, the method allows for the rapid regeneration of design solutions; (3) Designers can apply this highly flexible method to other design fields through fine-tuning. (4) The method optimizes the workflow of interior design.

This study investigates user experience (UX) sharing behaviors in the context of smartphone user interface (UI) design, emphasizing their significance for UI enhancement and effective marketing strategies. Grounded in the Stimulus–Organism–Response (SOR) framework, we examine how design perception attributes—perceived usability, novelty, enjoyment, and brand image—influence UX sharing, with a spotlight on the mediating role of individual motivation. A quantitative analysis (N = 472), Structural Equation Modeling (SEM), and mediation analysis were conducted. Our findings confirm that these components can positively impact UX sharing by bolstering personal expectations and self-efficacy in knowledge sharing, with perceived usability being an exception as it unexpectedly showed a negative association with sharing frequency. Moreover, perceived brand image and individual self-expectancy and self-efficacy enhance sharing outcomes. This research enriches our understanding of the strategic importance of user interface (UI) design in the context of smartphones, furnishing empirical grounding for devising sustainable UI design strategies and productive marketing tactics. Consequently, it bears considerable relevance to both theoretical insights and practical applications.

In-vehicle Augmented Reality Head-Up Displays (AR-HUDs) enhance driving performance and experience by presenting critical information such as navigation cues and collision warnings. Although many studies have investigated the efficacy of AR-HUD navigation and collision warning interface designs, existing research has overlooked the critical interplay between graphic spatial positioning and safety risks arising from inattentional blindness. This study employed a single-factor within-subjects design, with Experiment 1 and Experiment 2 separately examining the impact of the spatial planar position (horizontal planar position, vertical planar position, mixed planar position) of AR-HUD navigation graphics and collision warning graphics on drivers’ inattentional blindness. The results revealed that the spatial planar position of AR-HUD navigation graphics has no significant effect on inattentional blindness behavior or reaction time. However, the horizontal planar position yielded the best user experience with low workload, followed by the mixed planar position. For AR-HUD collision warning graphics, their spatial planar position does not significantly influence the frequency of inattentional blindness; From the perspectives of workload and user experience, the vertical planar position of collision warning graphics provides the best experience with the lowest workload, while the mixed planar position demonstrates superior hedonic qualities. Overall, this study offers design guidelines for in-vehicle AR-HUD interfaces.

The rising global aging population has increased the prevalence of Alzheimer’s disease (AD), a condition with limited treatment options, making non-p

Introduction: AI painting tools (AIPT) are creating new opportunities for creative aging, digital inclusion, and social participation among young-old adults. However, the psychological and social factors associated with their adoption remain underexplored. This study examined the factors associated with intention to use AIPT among adults aged 60–69 by extending the Technology Acceptance Model (TAM) with technological characteristics, social support, and individual motivational factors. Methods: Data from 450 valid respondents were analyzed using partial least squares structural equation modeling. Results: The results showed that output quality (OQ), relative advantage (RA), and trialability (TR) were positively associated with perceived usefulness (PU), with OQ showing the strongest association. Perceived ease of use (PEOU) was associated with RA, intergenerational support (IS), peer influence (PI), hedonic motivation (HM), and personal innovativeness (PIN), highlighting the importance of technological simplification, social support, and motivational readiness in older adults’ digital creative engagement. PEOU also showed a stronger association with intention to use (IU) than PU, suggesting that usability may be particularly salient in AIPT acceptance among young-old adults. Discussion: By situating technology acceptance in the context of creative aging, this study advances understanding of age-specific digital adoption and emphasizes the practical importance of age-friendly design, high-quality AI-generated output, and IS in supporting later-life creativity and inclusion.