Aims: Storytelling has evolved alongside human culture, giving rise to new media such as social robots. While these robots employ modalities similar to those used by humans, they can also utilize non-biomimetic modalities, such as color, which are commonly associated with emotions. As research on the use of colored light in robotic storytelling remains limited, this study investigates its integration through three empirical studies.

Methods: We conducted three studies to explore the impact of colored light in robotic storytelling. The first study examined the effect of emotion-inducing colored lighting in romantic storytelling. The second study employed an online survey to determine appropriate light colors for specific emotions, based on images of the robot’s emotional expressions. The third study compared four lighting conditions in storytelling: emotion-driven colored lights, context-based colored lights, constant white light, and no additional lighting.

Results: The first study found that while colored lighting did not significantly influence storytelling experience or perception of the robot, it made recipients felt more serene. The second study showed improved recognition of amazement, rage, and neutral emotional states when colored light accompanied body language. The third study revealed no significant differences across lighting conditions in terms of storytelling experience, emotions, or robot perception; however, participants generally appreciated the use of colored lights. Emotion-driven lighting received slightly more favorable subjective evaluations.

Conclusion: Colored lighting can enhance the emotional expressiveness of robots. Both emotion- driven and context-based lighting strategies are appropriate for robotic storytelling. Through this series of studies, we contribute to the understanding of how colored lights are perceived in robotic communication, particularly within storytelling contexts.

Aims: This study aims to enhance the design of augmented reality (AR) technologies for remote collaboration by examining the complex relationships among individual factors (user characteristics), psychological and physiological states during AR-mediated remote collaboration, and outcomes within an Input-Mediator-Output (IMO) model. The goal is to evaluate how individual characteristics influence psychological and physiological experiences, as well as task performance, in AR-mediated collaboration.

Methods: We hypothesize and evaluate an IMO model and use correlation analyses to examine the relationships among person-related input variables (e.g., predispositions, traits, attitudes, states, and contextual factors), psychological and physiological emergent states, and performance-related output variables.

Results: Our results demonstrate that individual characteristics significantly influence subjective experiences, physiological responses, and task performance, emphasizing the critical role of individual differences, alongside task- and technology-related factors, in shaping collaboration experiences and performance. These findings highlight the importance of considering individual characteristics in the design of AR tools to optimize user well-being and performance outcomes.

Conclusion: Our study provides a foundational framework for understanding the interplay between individuals, tasks, and technology, underscoring the need for AR tools that align with user characteristics. It also lays the groundwork for future IMO research in AR-mediated remote collaboration, contributing to the development of more effective and health-promoting AR technologies.