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The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable methods to accommodate diverse preferences and resolve conflicting directives. Drawing on conflict resolution theory, we introduce a user-centered workflow for multi-user personalization comprising three stages: Reflection, Analysis, and Feedback. We then present MAP—a Multi-Agent system for multi-user Personalization—to operationalize this workflow. By delegating subtasks to specialized agents, MAP (1) retrieves and reflects on relevant user information, while enhancing reliability through agent-toagent interactions, (2) provides detailed analysis for improved transparency and usability, and (3) integrates user feedback to iteratively refine results. Our user study findings (𝑛 = 12) highlight MAP’s effectiveness and usability for conflict resolution while emphasizing the importance of user involvement in resolution verification and failure management. This work highlights the potential of multi-agent systems to implement user-centered, multi-user personalization workflows and concludes by offering insights for personalization in multi-user contexts. 

With the increasing prevalence of online learning, adapting education to diverse learner needs remains a persistent challenge. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), promise powerful tools and capabilities to enhance personalized learning in online educational environments. In this work, we explore how LLMs can improve personalized learning experiences by catering to individual user needs toward enhancing the overall quality of online education. We designed personalization guidelines based on the growing literature on personalized learning to ground LLMs in generating tailored learning plans. To operationalize these guidelines, we implemented LearnMate, an LLM-based system that generates personalized learning plans and provides users with real-time learning support. We discuss the implications and future directions of this work, aiming to move beyond the traditional one-size-fits-all approach by integrating LLM-based personalized support into online learning environments. 

Automated planning is traditionally the domain of experts, utilized in fields like manufacturing and healthcare with the aid of expert planning tools. Recent advancements in LLMs have made planning more accessible to everyday users due to their potential to assist users with complex planning tasks. However, LLMs face several application challenges within end-user planning, including consistency, accuracy, and user trust issues. This paper introduces VeriPlan, a system that applies formal verification techniques, specifically model checking, to enhance the reliability and flexibility of LLMs for end-user planning. In addition to the LLM planner, VeriPlan includes three additional core features—a rule translator, flexibility sliders, and a model checker—that engage users in the verification process. Through a user study (𝑛 = 12), we evaluate VeriPlan, demonstrating improvements in the perceived quality, usability, and user satisfaction of LLMs. Our work shows the effective integration of formal verification and user-control features with LLMs for end-user planning tasks. 

Intergenerational co-creation using technology between grandparents and grandchildren can be challenging due to differences in technological familiarity. AI has emerged as a promising tool to support co-creative activities, offering flexibility and creative assistance, but its role in facilitating intergenerational connection remains underexplored. In this study, we conducted a user study with 29 grandparent-grandchild groups engaged in AI-supported story creation to examine how AI-assisted co-creation can foster meaningful intergenerational bonds. Our findings show that grandchildren managed the technical aspects, while grandparents contributed creative ideas and guided the storytelling. AI played a key role in structuring the activity, facilitating brainstorming, enhancing storytelling, and balancing the contributions of both generations. The process fostered mutual appreciation, with each generation recognizing the strengths of the other, leading to an engaging and cohesive co-creation process. We offer design implications for integrating AI into intergenerational co-creative activities, emphasizing how AI can enhance connection across skill levels and technological familiarity. 

Large-language models (LLMs) hold significant promise in improving human-robot interaction, offering advanced conversational skills and versatility in managing diverse, open-ended user requests in various tasks and domains. Despite the potential to transform human-robot interaction, very little is known about the distinctive design requirements for utilizing LLMs in robots, which may differ from text and voice interaction and vary by task and context. To better understand these requirements, we conducted a user study (n = 32) comparing an LLM-powered social robot against text- and voice-based agents, analyzing task-based requirements in conversational tasks, including choose, generate, execute, and negotiate. Our findings show that LLM-powered robots elevate expectations for sophisticated non-verbal cues and excel in connection-building and deliberation, but fall short in logical communication and may induce anxiety. We provide design implications both for robots integrating LLMs and for fine-tuning LLMs for use with robots. 

Abstract Corticosteroid-eluting endotracheal tubes (ETTs) were developed and employed in a swine laryngotracheal injury model to maintain airway patency and provide localized drug delivery to inhibit fibrotic scarring. Polycaprolactone (PCL) fibers with or without dexamethasone were electrospun onto the ETT surface PCL-only coated ETTs and placed in native airways of 18 Yorkshire swine. Regular and dexamethasone-PCL coated ETTs were placed in airways of another 18 swine injured by inner laryngeal mucosal abrasion. All groups were evaluated after 3, 7 and 14 days (n = 3/treatment/time). Larynges were bisected and localized stiffness determined by normal indentation, then sequentially matched with histological assessment. In the native airway, tissue stiffness with PCL-only ETT placement increased significantly from 3 to 7 days (p = 0.0016) and 3 to 14 days (p < 0.0001) while dexamethasone-PCL ETT placement resulted in stiffness decreasing from 7 to 14 days (p = 0.031). In the injured airway, localized stiffness at 14 days was significantly greater after regular ETT placement (23.1 ± 0.725 N/m) versus dexamethasone-PCL ETTs (17.10 ± 0.930 N/m,p < 0.0001). Dexamethasone-loaded ETTs were found to reduce laryngotracheal tissue stiffening after simulated intubation injury compared to regular ETTs, supported by a trend of reduced collagen in the basement membrane in injured swine over time. Findings suggest localized corticosteroid delivery allows for tissue stiffness control and potential use as an approach for prevention and treatment of scarring caused by intubation injury. 

Abstract Imaging spectroscopy is a powerful tool used to support diverse Earth science and applications objectives, ranging from understanding and mitigating widespread impacts of climate change to management of water at farm‐scale. Community studies, such as those deployed by NASA's Surface Biology and Geology and ESA's Copernicus Hyperspectral Imaging Mission for the Environment, have offered new and tangible insights into user needs that are then incorporated into overall mission planning and design. These technologies and tools will be key to develop and consolidate downstream services for users and resource management, given the current pressures on the environment posed by climate change and population growth. This process has highlighted the degree to which planned mission capabilities are responsive to community needs. In this study, we analyze user requirements belonging to the Italian Copernicus User Forum and to the user pool of NASA's Surface Biology and Geology community for the synergic use of hyperspectral imaging technology, providing a reference for the development of earth observation services and the consolidation of existing ones. In addition, potential cross‐mission coordination is analyzed to highlight key benefits—(a) addressing shared community needs around products requiring more frequent temporal revisit and (b) shared resources and community expertise around algorithm development. This paper discusses the critical role of early engagement with users to establish a community of practice ready to work with high spatial resolution imaging spectroscopy data sets. The main outcome is a guide for the synergetic use of hyperspectral mission and data together with the identification of the main gaps between user needs and satellite capabilities influencing the development of key national and trans‐national downstream services.