Olfactory stimuli have demonstrated the potential to evoke emotional depth and enhance user experiences in HCI. Yet, their role in shaping perceptions of social robots remains largely untapped. This study investigates how olfactory (scent) and auditory (voice) stimuli influence user perceptions of social robots. Using a 2x2 between-subjects design, participants interacted with a social robot under conditions with pleasant/unpleasant scents and friendly/unfriendly voices. The study measured perceived trust, friendliness, competence, and engagement. Our findings show that pleasant scents can enhance the perceptions of friendliness and engagement, while friendly voices can improve trust, friendliness, and engagement. The congruent combination of scents and voices affects friendliness and engagement but does not influence trust and competence. This study contributes to the growing work on multi-sensory Human-Robot Interaction (HRI) design, offering implications for creating more socially interactive robots.

Due to the limited internal space and economic costs, virtual keyboards(VKs) of mobile devices commonly use monotonous global sound and vibration feedback, resulting in unsatisfactory realism and negative experience. To mitigate the monotonous feedback of high-frequency keystrokes on VKs, we propose an interactive sound-vibrotactile feedback design. This system dynamically adjusts sound and vibrotactile re-sponses based on the user's typing speed. We conducted a user study (N=30) and found that the collaborative varied auditory-vibrotactile feedback provided a better experience, created a local feedback illusion, and improved global feedback. Additionally, users preferred subtle variations over distinct variations. Our method can improve global VK feedback and provide a better experience without increasing additional costs and burdens. This study contributes to improving the VK experience on low-cost mobile devices and provides design suggestions for VKs.

Selecting appropriate fonts for sentence-level text, such as slide titles and poster headings, requires pleasing glyphs and semantic congruence with the text. This task is especially challenging for users without design backgrounds. Previous studies established associations between tags and fonts but overlooked the different design intents between sentence-level text and word-level text influencing font selections. Recently, large language models (LLMs) like ChatGPT with extensive knowledge bases have emerged as promising solutions for various tasks. This study evaluated the capabilities of GPT-3.5 in recommending fonts compared to professional designers to explore the application of LLMs in the design field. We conducted comprehensive evaluations, including both quantitative metrics to assess font diversity, accuracy, and consistency, and a user study involving designers and non-designers. The results indicate that while GPT-3.5 performs well in selecting common serif and sans-serif fonts suitable for general use that align with the preferences of users without design backgrounds, it falls short in stylized font selection. Our findings highlight the potential and limitations of using GPT-3.5 for font selection in collaborative design environments and provide insights into integrating such models into design tools to support multidisciplinary teams.

Agentic AI-integrated robots are essential for effective, efficient, and safe Human-Robot collaboration (HRC), where robots must accurately interpret human behavior by understanding the working context. However, current human motion prediction models often focus on task-related context and rarely consider the robot as an influencing factor in HRC. This study navigates different contexts in human motion prediction for Agentic AI-empowered HRC, and proposes a robot-aware deep learning framework that integrates robot and task context into prediction. This framework handles context and human motions separately within a long short-term memory (LSTM)-based two-branch model to predict human motions in HRC tasks. The influence of different contextual information (e.g., robot actions, task-related object location) on prediction performance is also examined. The framework was implemented in a handover task and the results show that the proposed model improved performance by 7.95% in Average Displacement Error (ADE) and 8.74% in Final Displacement Error (FDE), compared to the baseline (i.e., without context). The findings indicate that context integration is critical for anticipating human motions, and the robot is an important context in HRC. This study advances the understanding of context integration in human motion prediction and contributes to the comprehension of AI-integrated robots in real-world HRC.

In the digital era, the significance of design education is on the rise due to its ability to cultivate creativity. However, the disconnect between design practice and theory, coupled with the abundance of design knowledge, poses challenges to learning in this field. Despite the potential of large language models (LLMs) to integrate various data sources for facilitating design knowledge dissemination, they face obstacles such as the scarcity of design-related datasets and limited natural language representations. To overcome these challenges, we introduce DRELM, a design-centric language model that serves as an assistant providing UI design recommendations. We also offer corresponding resources to advance language modeling research in the design domain. Importantly, DesignInstruct stands out as a premier dataset for guiding user interface tasks, while DesignEvaluation significantly contributes to autonomous design evaluation and decision support. In our research, we utilize supervised data from DesignInstruct and DesignEvaluation to fine-tune pre-trained Qwen-7B models for design tasks. Experiments conducted on test data affirm the effectiveness of our dataset in enhancing knowledge comprehension, design execution, and evaluation. We commit to making all training data and DRELM models at https://github.com/sssala/DRELM-A-Language-Model-for-Design-Recommendation-and-Evaluation.