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.

Olfactory interfaces are pivotal in HCI, yet their development is hindered by limited application scenarios, stifling the discovery of new research opportunities. This challenge primarily stems from existing design tools focusing predominantly on odor display devices and the creation of standalone olfactory experiences, rather than enabling rapid adaptation to various contexts and tasks. Addressing this, we introduce Mul-O, a novel task-oriented development platform crafted to aid semi-professionals in navigating the diverse requirements of potential application scenarios and effectively prototyping ideas. Mul-O facilitates the swift association and integration of olfactory experiences into functional designs, system integrations, and concept validations. Comprising a web UI for task-oriented development, an API server for seamless third-party integration, and wireless olfactory display hardware, Mul-O significantly enhances the ideation and prototyping process in multisensory tasks. This was verified by a 15-day workshop attended by 30 participants. The workshop produced seven innovative projects, underscoring Mul-O’s efficacy in fostering olfactory innovation.

Numerous studies have shown that integrating scents into movies enhances viewer engagement and immersion. However, creating such olfactory experiences often requires professional perfumers to match scents, limiting their widespread use. To address this, we propose OdorAgent which combines a LLM with a text-image model to automate video-odor matching. The generation framework is in four dimensions: subject matter, emotion, space, and time. We applied it to a specific movie and conducted user studies to evaluate and compare the effectiveness of different system elements. The results indicate that OdorAgent possesses significant scene adaptability and enables inexperienced individuals to design odor experiences for video and images.

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.

Large interactive displays are widely used in industrial scenarios to enhance ubiquitous and seamless human–machine interactions. However, few studies have paid attention to design implicit interaction that users can directly manipulate physical circumstance without touch or specific gesture. This article proposes ReflectU, a novel reflection-based approach that leverages mirror reflection for a natural and implicit interactive method for remote control, i.e., user will be able to directly interact with physical circumstance just via the reflection of their bare hands. We compare its performance with that of other two generally known devices: Wii Remoter and Microsoft Kinect. Moreover, performance metrics of ReflectU are evaluated in real-life scenarios and provide evidence in convincing performance in both the tasks requiring instant targeting and trajectory control. Furthermore, ReflectU is reported by users to be the most intuitive and satisfactory approach among all three candidates in user studies. Future industrial applications of the reflection-base mirror approach are discussed.

In this paper, we consider an intelligent reflecting surface (IRS)-assisted downlink cognitive radio (CR) system, in which a secondary access point (SAP) communicates with multiple secondary users (SUs) without affecting multiple primary users (PUs) in the primary network and all nodes are equipped with multiple antennas. Our design objective is to maximize the achievable weighted sum rate (WSR) of SUs subject to the total transmit power constraint at the SAP and the interference constraints at PUs, by jointly optimizing the transmit precoding at the SAP and the reflecting coefficients at the IRS. To deal with the complex objective function, the problem is reformulated by employing the well-known weighted minimum mean-square error (WMMSE) method and an alternating optimization (AO)-based algorithm is proposed. Furthermore, a special scenario with only a single PU and multiple SUs is considered and AO algorithm is adopted again. It is worth mentioning that the proposed algorithm has a much lower computational complexity than the above algorithm without the performance loss. Finally, some numerical simulations have been provided to demonstrate that the proposed algorithm outperforms other benchmark schemes.

When schools and families form a good partnership, children benefit. With the recent flourishing of communication apps, families and schools in China have shifted their primary communication channels to chat groups hosted on popular instant-messenger(IM) tools such as WeChat and QQ. With an interview study consisting of 18 parents and 9 teachers, followed by a survey study with 210 teachers, we found that IM group chat has become the most popular way that the majority of parents and teachers communicate, from among the many different channels available. While there are definite advantages to this kind of group chat, we also found a number of problematic issues, including a lack of privacy and repeated negative feedback shared by both parents and teachers. We discuss our results on how IM-based group chat could affect Chinese teachers’ authoritative figures, affect Chinese teacher’s work-life balance and potentially compromise Chinese students’ privacy.