Journaling is a well-established practice for reflecting on life events and constructing meaning; yet, finding concrete meaning from them remains challenging. Recent advances in generative AI demonstrate the potential to generate materials that support reflection, suggesting opportunities to assist meaning-making in journaling. However, prior approaches often center on ambiguous outputs and reflections of a single experience, leaving unexplored how concrete interpretations from AI can help self-reflection in journaling. In this study, we designed Augmentiary, an LLM-based journaling system that suggests candidate interpretations of experiences with concrete meaning while preserving users’ agency and voice, based on insights from a formative study with eight journal writers. We then conducted a four-week deployment study with 25 participants. Our findings show that AI’s interpretive feedback helped connect fragmented experiences and supported self-understanding through comparison with their own thoughts. Moreover, tensions between user agency and constructive reflection were revealed. We conclude by discussing design implications for AI-supported systems for fostering meaning-making without replacing users’ thinking.

In this paper, we present TubeLens, a system designed to support YouTube users in reflecting on how recommendation algorithms perceive and represent their interests. TubeLens invites users to engage with their algorithmic selves through self-portraits accompanied by dispositional keywords and explanations, creating space to consider how algorithmic experiences might be interpreted and potentially reshaped over time. Rather than positioning users as passive recipients of recommendations, TubeLens foregrounds users’ agency in questioning and making sense of algorithmic influence on their media consumption. We conducted an exploratory user study with 22 participants to examine users’ experiences with TubeLens. Our findings suggest that algorithmic self-portraits can surface gaps between perceived and algorithmic selves, supporting self-awareness and agentic awareness, while also revealing tensions around privacy and social comparison. This work offers initial insights into how interactive representations of algorithmic profiles can support reflective engagement with algorithmic systems and inform the design of future identity-oriented interfaces.

AI tools are increasingly used for UI evaluation, yet most treat evaluation as a single-pass, black-box process that limits both effective model reasoning and human involvement. Grounded in Situation Awareness (SA) theory, we reframe single-screen heuristic evaluation of mobile UIs as stagewise human–AI co-critique, structuring evaluation into three editable stages: Perception (what is on the screen), Comprehension (what elements mean and do), and Projection (what problems and fixes follow). We instantiate this framing in Criticmate, an interactive system that exposes intermediate reasoning artifacts for intervention. Across offline benchmarks and a controlled user study, we show that stagewise co-critique yields more expert-like and better balanced critiques than single-pass approaches, while supporting higher trust and engagement without reducing perceived autonomy.

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.