Fully autonomous vehicles (FAVs) operated by AI voice agents create a unique multi-user environment without a traditional driver, allowing users to engage in diverse activities such as entertainment and rest. In addition to the convenience of vehicle control, these agents face the challenge of resolving conflicts arising from multiple simultaneous user commands. This study investigates whether AI voice agents can effectively manage these command conflicts and enhance the multi-user experience (MUX). The research was conducted in two phases: In the preliminary study, online focus group interviews (FGIs) were conducted with 10 participants to explore their perceptions of using AI voice agents in FAVs. Participants shared their experiences and watched a concept video to discuss their expectations of FAV agents. Based on the FGI results, explainable AI voice agents were designed to prevent user conflicts between user commands, focusing on user tendencies and conflict contexts. In the main study, multi-user interactions with these agents were evaluated through online experiments with 89 participants. Two specific experiments were conducted based on the sources of conflict identified in the preliminary study: the first focused on control authority and the second on speech overlap. Participants watched scenario videos and assessed five variables, including sense of agency, trust, problem-solving ability, disappointment, and safety. The findings suggest that explanations provided by AI voice agents can effectively mitigate conflicts and improve MUX in FAVs. However, the effectiveness of these explanations varied across different contexts, indicating the need for alternative approaches. This research provides valuable insights for designing MUXs that prevent multi-user conflicts and meet user expectations in FAV.

This study investigates effective feedback mechanisms to maintain human engagement in interactive machine learning (IML) systems, focusing on social media platforms. We developed “Loop,” an IML system based on human-in-the-loop (HITL) principles that recommends content while encouraging users to report inaccuracies for model refinement. Loop implements three types of artificial intelligence (AI) feedback on user reports: (a) machine learning (ML)-centric, (b) personal-centric, and (c) community-centric feedback. In addition, we evaluated the relative effectiveness of these feedback types under two different task criticality scenarios: high and low. A user study with 30 participants was conducted to evaluate Loop through questionnaires and interviews. Results showed that participants preferred algorithmic improvements for personal benefit over altruistic contributions to the community, especially for low-criticality tasks. Furthermore, personal-centric feedback had a significant impact on user engagement and satisfaction. Our findings provide insights into the effectiveness of machine feedback in HITL-ML systems, contributing to the design of more engaging and effective IML interfaces. We discuss implications and strategies for encouraging proactive user engagement in HITL-ML-based systems, emphasizing the importance of tailored feedback mechanisms.

Music is intrinsically connected to human experience, yet the plethora of choices often renders the search for the ideal piece perplexing, especially when the search terms are ambiguous. This study questions the viability of employing visual data, specifically images, in innovative queries for music search, and it aims to better align search results with users’ moods and situational context. We designed and evaluated three prototype systems for music search—TTTune (text-based), VisTune (image-based), and VTTune (hybrid)—to comparatively assess user experience and system usability. In a comprehensive user study involving 236 participants, each participant interacted with one of the systems and subsequently completed post-experimental surveys. A subset of participants also participated in in-depth interviews to further elucidate the potential and the advantages of image-based music retrieval (IMR) systems. Our findings reveal a marked preference for the user experience and usability offered by the IMR approach, as compared with the traditional text-based method. This underscores the potential of the image in an effective search query. Based on these findings, we discuss interface design guidelines tailored for IMR systems and factors affecting system performance, contributing to the evolving landscape of music search methods.

Delivery robots are increasingly becoming part of our urban landscape. However, the general public is divided about their presence in public spaces; some welcome their usefulness, while others see them as intrusive or even threatening. This study aims to understand users' perceptions of these robots to provide concrete insights into their further development. First, we used text mining to analyze people's reactions to popular YouTube videos featuring delivery robots. Based on these findings, we applied the scenario-based design method to develop scenarios illustrating user interactions with delivery robots. We then conducted in-depth interviews with 30 participants to explore their views on these scenarios. Our analysis highlighted several design issues, including robots' aesthetics, interactions with pedestrians, and the broader physical and regulatory framework. We also identified common concerns and positive expectations for these robots. From these findings, we propose design implications for the future of delivery robots.