Personalization is crucial for effective learning, yet online learning, designed for widespread availability and open access, lacks personalized guidance. Recent advancements in large language models (LLMs) offer opportunities to bridge this gap. We explore how LLM-driven tools may be designed to support personalized and adaptive learning and examine how they shape user experience and learning outcomes. We iteratively designed LearnMate2 to support online learning by providing personalized study plans, real-time contextual assistance, and adaptive learning activities. A preliminary study (n = 24) assessed the effectiveness and usability of LearnMate2 and informed refinements in our system, which we then evaluated (n = 16) against a combination of a state-of-the-art online learning platform and an LLM for learning support. Results indicate that LearnMate2 advances AI pedagogy by improving both learning outcomes and user experience compared to existing online learning and support tools. This work advances our understanding of the design space of personalized, AI-driven educational tools and their potential impact on user experience.

Advances in AI have enabled robots to engage in flexible, multi-turn dialogue. Yet many scenarios require robots to utilize additional modalities that complement speech to convey complex information. Robot screens can meet this need by supporting parallel processing, quick verification, and richer representations. As robots are integrated into an increasing number of scenarios with complex communication requirements, there is a need to systematically examine how screens may be used and designed to complement and augment verbal communication. In this paper, through an analysis of 357 commercial and research robots, we outline an initial design space of robot screens. Building on this design space, we present findings from two studies: a co-design study (n = 12) that explored user preferences for screen designs and derived a set of design guidelines; and an online pilot study (n = 89) that integrated these guidelines into screen designs and evaluated how these designs shaped user perceptions of robot communication. Our contributions include a database of screen-equipped robots; the design space of and guidelines for informational robot screens; and an empirical understanding of user perceptions on the use of robot screens.

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.

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.

Increasing evidence suggests that many deployed AI systems do not sufficiently support end-user interaction and information needs. Engaging end-users in the design of these systems can reveal user needs and expectations, yet effective ways of engaging end-users in the AI explanation design remain under-explored. To address this gap, we developed a design method, called AI-DEC, that defines four dimensions of AI explanations that are critical for the integration of AI systems—communication content, modality, frequency, and direction—and offers design examples for end-users to design AI explanations that meet their needs. We evaluated this method through co-design sessions with workers in healthcare, finance, and management industries who regularly use AI systems in their daily work. Findings indicate that the AI-DEC effectively supported workers in designing explanations that accommodated diverse levels of performance and autonomy needs, which varied depending on the AI system’s workplace role and worker values. We discuss the implications of using the AI-DEC for the user-centered design of AI explanations in real-world systems.

Robots in real-world environments continuously engage with multiple users and encounter changes that lead to unexpected conflicts in fulfilling user requests. Recent technical advancements (e.g., large-language models (LLMs), program synthesis) offer various methods for automatically generating repair plans that address such conflicts. In this work, we understand how automated repair and explanations can be designed to improve user experience with robot failures through two user studies. In our first, online study (n = 162), users expressed increased trust, satisfaction, and utility with the robot performing automated repair and explanations. However, we also identified risk factors—safety, privacy, and complexity—that require adaptive repair strategies. The second, in-person study (n = 24) elucidated distinct repair and explanation strategies depending on the level of risk severity and type. Using a design-based approach, we explore automated repair with explanations as a solution for robots to handle conflicts and failures, complemented by adaptive strategies for risk factors. Finally, we discuss the implications of incorporating such strategies into robot designs to achieve seamless operation among changing user needs and environments.

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.

Social robots are increasingly introduced into children’s lives as educational and social companions, yet little is known about how these products might best be introduced to their environments. The emergence of the “unboxing” phenomenon in media suggests that introduction is key to technology adoption where initial impressions are made. To better understand this phenomenon toward designing a positive unboxing experience in the context of social robots for children, we conducted three field studies with families of children aged 8 to 13: (1) an exploratory free-play activity (n = 12); (2) a co-design session (n = 11) that informed the development of a prototype box and a curated unboxing experience; and (3) a user study (n = 9) that evaluated children’s experiences. Our findings suggest the unboxing experience of social robots can be improved through the design of a creative aesthetic experience that engages the child socially to guide initial interactions and foster a positive child-robot relationship.