Reminiscence has been shown to provide benefits for older adults, but traditionally relies on personal photos as memory cues and interactions with real people who may not always be available. We present ReminiBuddy, a novel LLM-powered multi-agent conversational system, which allows older adults to engage with two distinct agents - one embodying an older identity and the other a younger identity - while using not only personal photos but also 3D models of generic nostalgic objects as memory cues. Our study, with older adult participants, found that the conversational approach both enjoyable and beneficial for reminiscence. While the younger agent was perceived as more emotionally engaging, the older one fostered greater resonance in content. Personal photos prompted autobiographical memories, whereas 3D generic nostalgic objects evoked shared memories of an era, contributing to a more multifaceted reminiscence experience. We further present design implications for better supporting older adults in reminiscing with LLM-powered conversational agents.

The proliferation of AI-powered search and recommendation systems has accelerated the formation of “filter bubbles” that reinforce people’s biases and narrow their perspectives. Previous research has attempted to address this issue by increasing the diversity of information exposure, which is often hindered by a lack of user motivation to engage with. In this study, we took a human-centered approach to explore how Large Language Models (LLMs) could assist users in embracing more diverse perspectives. We developed a prototype featuring LLM-powered multi-agent characters that users could interact with while reading social media content. We conducted a participatory design study with 18 participants and found that multi-agent dialogues with gamification incentives could motivate users to engage with opposing viewpoints. Additionally, progressive interactions with assessment tasks could promote thoughtful consideration. Based on these findings, we provided design implications with future work outlooks for leveraging LLMs to help users burst their filter bubbles.

Work is becoming more and more flexible nowadays. It can take place in the office, at home, or even on the go; tasks may encompass activities in the form of documents, multimedia, or 3D models in virtual space. Under such circumstances, personal computers (PC), the most widely used productivity devices for work today, cannot well address the diverse needs such as screen size, privacy, and flexibility to display diverse content formats (e.g., 2D to 3D), due to their fixed hardware specs. We believe the solution lies in Extended Reality (XR). In this article, we explored how XR glasses can be used for PC’s virtual extended displays, and conducted user interviews and usability tests to propose a systematic user experience design and evaluation framework. We discovered that the design space encompasses four dimensions general placement, display specs, operating system integration, and interaction behaviors) and summarized users’ corresponding preferences. We proposed a quality-of-experience (QoE) evaluation framework for XR virtual displays consisting of visual quality, visual fatigue and discomfort, as well as immersiveness, and identified clarity as the most significant factor that affects user satisfaction. Our design and evaluation frameworks could serve as a resource for both practitioners and scholars with an interest in the design and evaluation of virtual displays.

Evidence for positivity and optimism bias abounds in high-level belief updates. However, no consensus has been reached regarding whether learning asymmetries exist in more elementary forms of updates such as reinforcement learning (RL). In RL, the learning asymmetry concerns the sensitivity difference in incorporating positive and negative prediction errors (PE) into value estimation, namely the asymmetry of learning rates associated with positive and negative PEs. Although RL has been established as a canonical framework in characterizing interactions between agent and environment, the direction of learning asymmetry remains controversial. Here, we propose that part of the controversy stems from the fact that people may have different value expectations before entering the learning environment. Such a default value expectation influences how PEs are calculated and consequently biases subjects' choices. We test this hypothesis in two learning experiments with stable or varying reinforcement probabilities, across monetary gains, losses, and gain-loss mixed environments. Our results consistently support the model incorporating both asymmetric learning rates and the initial value expectation, highlighting the role of initial expectation in value updating and choice preference. Further simulation and model parameter recovery analyses confirm the unique contribution of initial value expectation in accessing learning rate asymmetry.