Walking aids are critical for people with mobility impairments, yet current options remain unsatisfactory. Static knee braces are lightweight and affordable, but their rigid joints force users into unnatural gait patterns, leading to fatigue, reduced safety, and high abandonment rates. Robotic exoskeletons, in contrast, offer dynamic assistance that adapts to gait phases but rely on sensors, motors, and batteries that make them heavy, complex, and prohibitively expensive.

CHI ’25, Yokohama, Japan

Block-building activities are crucial for developing children's spatial reasoning and mathematical skills, yet parents often lack the expertise to guide these activities effectively. BrickSmart, a pioneering system, addresses this gap by providing spatial language guidance through a structured three-step process: Discovery & Design, Build & Learn, and Explore & Expand. This system uniquely supports parents in 1) generating personalized block-building instructions, 2) guiding parents to teach spatial language during building and interactive play, and 3) tracking children's learning progress, altogether enhancing children's engagement and cognitive development. In a comparative study involving 12 parent-child pairs children aged 6-8 years) for both experimental and control groups, BrickSmart demonstrated improvements in supportiveness, efficiency, and innovation, with a significant increase in children's use of spatial vocabularies during block play, thereby offering an effective framework for fostering spatial language skills in children.

Creative Problem-Solving (CPS) promotes creative and critical thinking while enhancing real-world problem-solving skills, making it essential for middle school education.However, providing personalized mentorship in CPS projects at scale is challenging due to resource constraints and diverse student needs.To address this, we developed Mentigo, an AI-driven mentor agent designed to guide middle school students through the CPS process.Using a dataset of real classroom interactions, we encoded CPS task stages, adaptive guidance strategies, and personalized feedback mechanisms to inform Mentigo's dynamic mentoring framework powered by large language models (LLMs).A comparative experiment with 12 students and evaluations from five expert educators demonstrated improved student engagement, creativity, and task performance.Our findings highlight design implications for using LLM-based AI mentors to enhance CPS learning in educational environments.

Problematic smartphone use negatively affects physical and mental health. Despite the wide range of prior research, existing persuasive techniques are not flexible enough to provide dynamic persuasion content based on users’ physical contexts and mental states. We first conducted a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia. This informs our design of four persuasion strategies: understanding, comforting, evoking, and scaffolding habits. We leveraged large language models (LLMs) to enable the automatic and dynamic generation of effective persuasion content. We developed MindShift, a novel LLM-powered problematic smartphone use intervention technique. MindShift takes users’ in-the-moment app usage behaviors, physical contexts, mental states, goals & habits as input, and generates personalized and dynamic persuasive content with appropriate persuasion strategies. We conducted a 5-week field experiment (N=25) to compare MindShift with its simplified version (remove mental states) and baseline techniques (fixed reminder). The results show that MindShift improves intervention acceptance rates by 4.7-22.5% and reduces smartphone usage duration by 7.4-9.8%. Moreover, users have a significant drop in smartphone addiction scale scores and a rise in self-efficacy scale scores. Our study sheds light on the potential of leveraging LLMs for context-aware persuasion in other behavior change domains.