注目の論文一覧

As Artificial Intelligence (AI) conversational agents become widespread, people are increasingly using them for health information seeking. The use of off-the-shelf conversational agents for health information seeking could place high metacognitive demands (the need for extensive monitoring and control of one's own thought process) on individuals, which could compromise their experience of seeking health information. However, currently, the specific demands that arise while using conversational agents for health information seeking, and the strategies people use to cope with those demands, remain unknown. To address these gaps, we conducted a think-aloud study with 15 participants as they sought health information using our off-the-shelf AI conversational agent. We identified the metacognitive demands such systems impose, the strategies people adopt in response, and propose considerations for designing beyond off-the-shelf interfaces to reduce these demands and support better user experiences and affordances in health information seeking.

Systems' latency — the time between user input and system response — slows down the human-computer interaction loop. Several studies revealed negative objective and subjective effects of high latency, typically treating latency as a constant delay. Because latency varies significantly in practice, recent work also assessed the effects of large and sudden latency changes. In practice, however, latency variations are small but frequent. As the effects of such variations are unclear, we investigate how small latency variations (+/- 50 ms) affect users' performance and perceived task load for 2D target selection tasks with static and moving targets. For static targets, we found that latency variation causes significantly higher completion times and less efficient trajectories, however with small effect sizes. In contrast, we found no significant effects on any performance measure for moving targets. Our findings indicate that the effect of latency variation is generally very small and quickly disappears for non-trivial tasks.

Responsiveness in large language model (LLM) applications is widely assumed to be critical, yet the impact of latency on user behavior and perception of output quality has not been systematically explored. We report a controlled experiment varying time-to-first-token latency (2, 9, 20 seconds) across two taxonomy-driven knowledge task types (Creation and Advice). Log analyses reveal that user interaction behaviors were robust to latency, yet varied by task type: Creation tasks elicited more frequent prompting than Advice tasks. In contrast, participants who experienced 2-second latencies rated the LLM’s outputs less thoughtful and useful than those who experienced 9- or 20-second latencies. Participants attributed delays to AI deliberation, though long waits occasionally shifted this interpretation toward frustration or concerns about reliability. Overall, this work demonstrates that latency is not simply a cost to reduce but a tunable design variable with ethical implications. We offer design strategies for enhancing human-LLM interaction.

The use of AI in product design during early creative phases raises questions about its long-term consequences. Concerns are that extended AI use might inhibit creative cognitive processes, especially in novice designers. The aim of this study is to contribute to ongoing research in creative cognition and creative support tools such as AI in design. We conducted an exploratory study with 61 undergraduate students to analyze design exploration in sketching versus AI concept generation. The results indicate that AI groups produced a higher quantity and variation of total ideas (including text-based ideas), while sketch groups generated more image-based ideas. It was inconclusive whether the final image concepts from both AI and sketch groups were more creative. Additionally, homogenization effects were observed in the AI groups. Moreover, while the evolution of the design intent was evident in students who sketched, the focus in AI groups appeared to shift towards the tool (AI), which we analyzed as different design space exploration (DSE) prompting styles.

In interpersonal interactions, individuals often exhibit avoidance behaviors toward others they find unpleasant, which can undermine the comfort of everyday social experiences. Existing human-computer interaction (HCI) research has primarily focused on promoting social connections, while support for avoidance-oriented social situations remains underexplored. To address this gap, we propose leveraging Diminished Reality (DR) technology to obscure perceptual cues of undesirable individuals. We designed and implemented a mixed reality prototype system and conducted experiments manipulating both the occlusion method and social distance. Results indicate that DR significantly reduces users' social anxiety and sense of social presence. Moreover, participants generally expressed positive attitudes toward usage intention and ethical considerations. This work extends HCI research on social comfort, shifting the focus from "facilitating connection" to "supporting avoidance".