Children with high-functioning autism (HFA) face significant challenges in developing social skills. Over the years, some technology-assisted therapies for those children were developed and have proven effective. However, in real-world practices, these therapies have limitations, such as lacking of attractiveness to children. Therefore, we propose HAN, an interactive robot designed to teach social skills to children with HFA based on TIP (Teaching Interaction Procedure) therapy. We aim to design an attractive appearance and a series of attractive interfaces to provide a better therapy experience for children with HFA. We structured social interactions through teaching, interaction, feedback, and generalization; thus, HAN systematically taught a wide range of social skills through a comprehensive curriculum. We conducted a preliminary user test at a local autism intervention center and collected valuable user feedback.

A robot with emotional intelligence might detect frustration in a user's tone and respond with supportive or motivational language, fostering trust and emotional bonds. This study explores users' expectations of emotional intelligence in social robots. Using the Wukong robot and the Hume.AI platform, we conducted a study with surveys and contextual interactions. Data were collected from 30 participants in the US and China. The preliminary findings indicate that emotional in-telligence in robots plays a critical role in meeting both functional and emotional needs, shaping expectations, and fostering trust. However, the expectations of users towards enhanced emotional intelligence are diversified.

Reducing electronic waste is one of the key topics in Sustainable Interaction Design. However, research regarding the sustainable future of the long-term use of robots is limited. Our study employs a game-based workshop to investigate the factors influencing potential users’ sustainability choices in long-term human-robot interactions. We developed a board game called “Game of Life with Your Companion Robot” to help participants situate themselves in the context of cohabiting with companion robots of their choice. Through five workshops with seventeen participants, we explore (a) the factors mentioned by participants that influence their sustainability choices in long-term human-robot interactions, and (b) the connections between how participants frame their companion robots and their sustainability choices. We use four sustainable criteria to evaluate participants’ choices. Our findings show that different framings of robots can result in different sustainable outcomes.

Fashion serves as a means to not only present an enhanced version of oneself but also to actively become a better individual through its influence. Meanwhile, the rapid development of AI technology has brought more possibilities in the tech-assisted personal fashion domain. We review the literature regarding imitation theory, fashion psychology, and the changes in fashion paradigms. Leveraging these theories, we propose a future personalized fashion solution: a fashion stylist chatbot that is capable of generating inspirational fashion styles on virtual representations of our bodies. Differentiating from previous work, this solution can help us build our wardrobe starting from thinking about our psychosocial aspects.

Robots do not need clothes to keep them warm or for other purely functional reasons; nevertheless, certain people choose to clothe their home robots. This paper contributes to an emerging interest in understanding the practice of clothing robots by analyzing how people dress up the pet-type companion robot aibo. We study a collection of 2320 Twitter posts with photos containing aibo robots in clothes provided for them by their owners. The collection of tweets spans four years (2018-2021). We report our findings regarding aibo clothing types, fashion trends, clothing choices in different use scenarios, and clothing sources. Our study provides insights into the benefits of clothing robots for users, different modes of robot fashion, and how users’ current practices of robot clothing design and use can inform the future design of companion robots.

This abstract outlines the research trajectory during my doctoral study, including completed work and future plans. My primary research focus is fashion and sustainable practices in the context of companion robots. After exploring people’s spontaneous behaviors of dressing up their robots, I want to further extend the connotation of robot fashion and use fashion and sustainability as frameworks to explore how people treat their companion robots.

Systems worldwide deploy CAPTCHAs as a security mechanism to protect from unauthorized automated access. Typically, the effectiveness of CAPTCHAs is evaluated based on their resilience against bots. User perceptions of the interactive experience and effectiveness of CAPTCHAs have received less attention, especially for comparing the variations of CAPTCHAs presented in different regions across the world. As the first step toward filling this gap, we conducted semi-structured interviews with ten participants fluent in Chinese and English to investigate whether user perceptions are affected by variations in CAPTCHAs presented in China and the United States, respectively. We found notable differences in the perceived user experience and effectiveness across the different CAPTCHA types, but not across regional variations of the same type. Our findings point to a number of avenues for making the CAPTCHA user experience more universal and inclusive.

There is an increasing need to prepare young learners to be Artificial Intelligence (AI) capable for the future workforce and everyday life. Machine Learning (ML), as an integral subfield of AI, has become the new engine that revolutionizes practices of knowledge discovery. Making ML experience accessible to young learners, however, remains challenging due to its high demand for mathematical and computational skills. This research focuses on designing novel learning environments that help demystify ML technologies for K-12 students, and also investigating new opportunities for maximizing ML accessibility through integration with scientific discovery in STEM education. We developed SmileyCluster - a hands-on and collaborative learning environment that utilizes glyph-based data visualization and superposition comparative visualization to assist learning an entry-level ML technology, namely k-means clustering. Findings from an initial case study with high school students in a pre-college summer program show that SmileyCluster leads to positive change in learning ML concepts, methods and sense-making of patterns. Findings of this study also shed light on understanding ML as a data-enabled approach to support evidence-based scientific discovery in K-12 STEM education.