Zhuoyang Li

Personal Affective Physicalization is the process by which individuals express emotions through tangible forms to record, reflect on, and communicate. Yet such physical data representations can be challenging to design due to the abstract nature of emotions. Given the shown potential of AI in detecting emotion and assisting design, we explore opportunities in AI-assisted design of personal affective physicalization using a Research-through-Design method. We developed PhEmotion, a tool for embedding LLM-extracted emotion values from human-AI conversations into parametric design of physical artifacts. A lab study was conducted with 14 participants creating these artifacts based on their personal emotions, with and without AI support. We observed nuances and variations in participants' creative strategies, meaning-making processes and their perceptions of AI support in this context. We found key tensions in AI-human co-creation that provide a nuanced agenda for future research in AI-assisted personal affective physicalization.

Amidst the increasingly prevalent smartphone addiction, we introduce StayFocused, a mobile app to help people focus on their tasks at hand by reducing compulsive smartphone use. Besides guiding people to set focus sessions for non-screen time, we incorporated reflective prompts probing individuals’ phone-checking intentions whenever they check their phones and a chatbot to deliver these prompts. To examine the effects of the reflective prompts and the chatbot support, we designed three versions of StayFocused: baseline, reflection, and reflection-chatbot, and conducted a stage-based between-subjects study with 36 college students over five weeks. We found that participants who received the reflective prompts were able to focus longer and resist distractions, and those with chatbot support seemed to better maintain their smartphone use reduction. By highlighting how participants reflected on their focus session activities and their preferences for the chatbot, we discuss the implications of designing persuasive conversational interfaces to reduce unintended behaviors.

Conversational agents (CAs) have become ubiquitous in our daily lives. Recognizing the potential of CAs being persuasive agents, we are interested in leveraging CAs to reduce compulsive smartphone use, a widespread behavior among young adults that can lead to negative consequences. This work presents the design and development of StayFocused, a mobile app incorporating a chatbot to assist people in setting focus goals and reflecting on their phone-checking behaviors in situ. Particularly, we highlight the iterative process of curating prompts for GPT-3, and the lessons learned from our trials and errors. With StayFocused, we propose a three-week between-subjects study with college students. We envision the design of StayFocused and the proposed study will deepen our understanding of how CAs support immediate actions as well as sustained behavior change, and inform the design of persuasive technologies for reducing unintended behaviors such as compulsive smartphone use.