Despite the growing potential of artificial intelligence (AI) in improving clinical decision making, patients' perspectives on the use of AI for their care decision making are underexplored. In this paper, we investigate patients' preferences towards the autonomy of AI in assisting healthcare decision making. We conducted interviews and an online survey using an interactive narrative and speculative AI prototypes to elicit participants' preferred choices of using AI in a pregnancy care context. The analysis of the interviews and in-story responses reveals that patients' preferences for AI autonomy vary per person and context, and may change over time. This finding suggests the need for involving patients in defining and reassessing the appropriate level of AI assistance for healthcare decision making. Departing from these varied preferences for AI autonomy, we discuss implications for incorporating patient-centeredness in designing AI-powered healthcare decision making.

As sleep health is increasingly becoming important in recent years, many wearable products and mobile apps have been developed to track users' sleep and interpret their sleep quality. Most of the available designs have mainly focused on objective measurements, such as body movement, heart rate, and/or bedroom light, noise level, and temperature. However, due to the lack of users' subjective experience measurements, sleep trackers often fail to provide useful suggestions for improving their sleep. In this study, we developed SlumberBot with conversational chatbot technology to help users capture their subjective sleep experiences and relevant factors in daytime activities as well. With SlumberBot, we conducted a preliminary field study with five participants in a 4-week time period. The result shows that SlumberBot is easy to stay engaged with and supportive of users' self-reflection on contextual factors related to sleep quality. Besides, SlumberBot has shown the potential of triggering short-term behavior changes that would impact their sleep positively.

While sleep health is increasingly becoming important to many people, it is challenging to maintain good sleep quality. Because sleep can be affected by many internal and external factors, which may differ among individuals, it is therefore important to support people to explore and reflect on what factors truly affect their sleep as most existing sleep self-tracking tools mainly focus on showing how individuals slept. In this study, we developed two design probes to support users to investigate personal sleep factors through self-experimenting and reflection. The tangible probe enables individuals to track three personal sleep factors flexibly, while the digital visualization one provides weekly reports of their sleep together with the three tracked factors. We conducted a preliminary field study with five participants over four weeks. The results show that our designs could support users to investigate their personal concerned sleep factors in terms of the causal relationship with the subjective sleep quality.

Intelligent agents have become prevalent in everyday IT products and services. To improve an agent’s knowledge of a user and the quality of personalized service experience, it is important for the agent to cooperate with the user (e.g., asking users to provide their information and feedback). However, few works inform how to support such user-agent co-performance from a human-centered perspective. To fill this gap, we devised Co-Performing Agent, a Wizard-of-Oz-based research probe of an agent that cooperates with a user to learn by helping users to have a partnership mindset. By incorporating the probe, we conducted a two-month exploratory study, aiming to understand how users experience co-performing with their agent over time. Based on the findings, this paper presents the factors that affected users’ co-performing behaviors and discusses design implications for supporting constructive co-performance and building a resilient user–agent partnership over time.