Robot appearance crucially shapes Human-Robot Interaction (HRI) but is typically described via broad categories like anthropomorphic, zoomorphic, or technical. More precise approaches focus almost exclusively on anthropomorphic features, which fail to classify robots across all types, limiting the ability to draw meaningful connections between robot design and its effect on interaction. In response, we present METAMORPH, a comprehensive framework for classifying robot morphology. Using a metamodeling approach, METAMORPH was synthesized from 222 robots in the IEEE Robots Guide, offering a structured method for comparing visual features. This model allows researchers to assess the visual distances between robot models and explore optimal design traits tailored to different tasks and contexts.

This study explores which factors of the visual design of a robot may influence how humans would place it in a collaborative cooking scenario and how these features may influence task delegation. Human participants were placed in a Virtual Reality (VR) environment and asked to set up a kitchen for cooking alongside a robot companion while considering the robot's morphology. We collected multimodal data for the arrangements created by the participants, transcripts of their think-aloud as they were performing the task, and transcripts of their answers to structured post-task questionnaires. Based on analyzing this data, we formulate several hypotheses: humans prefer to collaborate with biomorphic robots; human beliefs about the sensory capabilities of robots are less influenced by the morphology of the robot than beliefs about action capabilities; and humans will implement fewer avoidance strategies when sharing space with gracile robots. We intend to verify these hypotheses in follow-up studies.

As the gaming industry advances, it seeks increased immersion. Concurrently, recent advancements in speech recognition substantially enhanced its accuracy, creating opportunities for speech-based interactions in gameplay. Integrating speech into single-player games with non-player characters (NPCs) introduces a new social dimension. Yet, like toxicity in social media and multiplayer games, anti-social behavior can infiltrate NPC interactions, generating a toxic gaming atmosphere. We designed a speech-based game called “A Day at the Office” and conducted a within-subject study with 26 participants to explore player interactions with NPCs possessing different human qualities. Furthermore, we introduced courtesy-based game mechanics, where NPCs react to players’ choice of words and loudness, investigating player experiences. Results revealed that participants spoke differently with NPCs based on their human qualities, such as gender and hierarchical roles. Moreover, the courtesy-based interactions fostered greater cognitive engagement, suggesting that tactful interactions contribute to enhanced immersion and presence in the game.

Recent advances in language processing and speech recognition open up a large opportunity for video game companies to embrace voice interaction as an intuitive feature and appealing game mechanics. However, speech-based systems still remain liable to recognition errors. These add a layer of challenge on top of the game’s existing obstacles, preventing players from reaching their goals and thus often resulting in player frustration. This work investigates a novel method called context-aware speech recognition, where the game environment and actions are used as supplementary information to enhance recognition in a speech-based game. In a between-subject user study (N = 40), we compared our proposed method with a standard method in which recognition is based only on the voice input without taking context into account. Our results indicate that our proposed method could improve the player experience and the usability of the speech system.

This study explores the prevalence of dark patterns in mobile games that exploit players through temporal, monetary, social, and psychological means. Recognizing the ethical concerns and potential harm surrounding these manipulative strategies, we analyze user-generated data of 1496 games to identify relationships between the deployment of dark patterns within "dark" and "healthy" games. Our findings reveal that dark patterns are not only widespread in games typically seen as problematic but are also present in games that may be perceived as benign. This research contributes needed quantitative support to the broader understanding of dark patterns in games. With an emphasis on ethical design, our study highlights current problems of revenue models that can be particularly harmful to vulnerable populations. To this end, we discuss the relevance of community-based approaches to surface harmful design and the necessity for collaboration among players/users and practitioners to promote healthier gaming experiences.

In the rapidly evolving field of artificial intelligence (AI) agents, designing the agent's characteristics is crucial for shaping user experience.This workshop aims to establish a research community focused on AI agent persona design for various contexts, such as in-car assistants, educational tools, and smart home environments.We will explore critical aspects of persona design, such as voice, embodiment, and demographics, and their impact on user satisfaction and engagement.Through discussions and hands-on activities, we aim to propose practices and standards that enhance the ecological validity of agent personas.Topics include the design of conversational interfaces, the influence of agent personas on user experience, and approaches for creating contextually appropriate AI agents.This workshop will provide a platform for building a community dedicated to developing AI agent personas that better fit diverse, everyday interactions.

Reflecting on personal challenges can be difficult. Without encouragement, the reflection process often remains superficial, thus inhibiting deeper understanding and learning from past experiences. To allow people to immerse themselves in and deeply reflect on past challenges, we developed SelVReflect, a VR experience which offers active voice-based guidance and a space to freely express oneself. SelVReflect was developed in an iterative design process (N=5) and evaluated in a user study with N=20 participants. We found that SelVReflect enabled participants to approach their challenge and its (emotional) components from different perspectives and to discover new relationships between these components. By making use of the spatial possibilities in VR, participants developed a better understanding of the situation and of themselves. We contribute empirical evidence of how a guided VR experience can support reflection. We discuss opportunities and design requirements for guided VR experiences that aim to foster deeper reflection.

While a growing number of households contain home assistants, they mainly remain voice-only devices where the virtual agent is not represented visually. The visual representation of the agent is limited to the device's housing and abstract light animations that signify the assistant's state to its users. However, the audio channel is limited in conveying information beyond semantic content. Embodied virtual assistants can enhance interaction with conversational interfaces by adding a visual layer to further convey personality and human characteristics. In this work, we conducted an online survey (N=78) to explore people's preferences for visualizing humanoid assistants. Our findings suggest that participants prefer an agent who appears mature, healthy, competent, and attractive. Furthermore, demographic similarities between the users and agents are wished for the agent to look more relatable. We discuss these findings and their implications for the design of virtual humanoid home assistants.

With rapid advances in artificial intelligence and natural language processing, voice assistants are evolving into advanced digital personal assistants capable of complex tasks. As they become more proficient at understanding people’s behaviors, preferences, intentions, and surroundings, opportunities for proactive interactions emerge. However, despite their potential benefits, people still find certain proactive agent interactions inappropriate and invasive, such as correcting or nudging the user. This study investigates humor’s potential to enhance the desirability of proactive agent comments, given its stress-relieving and acceptance-promoting characteristics. We investigate how infusing humor into VA statements affects perceptions of appropriateness and desirability in proactive interventions. We designed storyboards showcasing voice assistants’ proactive actions in everyday situations and social contexts. Participants () assessed these scenarios in an online questionnaire across multiple criteria. Our results reveal that humor’s impact on proactive statement desirability is contingent on participants’ perceptions of voice assistants and their subjective judgment of the humor.

Advances in speech recognition, language processing and natural interaction have led to an increased industrial and academic interest. While the robustness and usability of such systems are steadily increasing, speech-based systems are still susceptible to recognition errors. This makes intelligent error handling of utmost importance for the success of those systems. In this work, we integrated anticipatory error handling for a voice-controlled video game where the game would perform a locally optimized action in respect to goal completion and obstacle avoidance, when a command is not recognized. We evaluated the user experience of our approach versus traditional, repetition-based error handling (). Our results indicate that implementing anticipatory error handling can improve the usability of a system, if it follows the intention of the user. Otherwise, it impairs the user experience, even when deciding for technically optimal decisions.

The use of voice assistants (VAs) is spreading widely. Most common VAs consist of a single, usually female voice that responds to the user’s inquiry. We designed a VA system appearing as a group of agents, each with a different voice and a specialized task domain. We conducted a quantitative user study comparing our multi-agent approach with a conventional single-agent assistant in a smart home scenario as virtual reality (VR) simulation. The results show significantly higher user experience ratings for the multi-agent concept. Based on our findings, we discuss the potentials and challenges of designing multi-party VA systems.