Large language models (LLMs) are popular tools for creative ideation, but have been shown to homogenize outputs across users. We test if approaching an AI tool with high human (vs. low) human agency can mitigate this homogenization effect by encouraging people to use AI to augment their creativity, rather than offload it. Participants were experimentally assigned to one of three conditions (high-agency approach with AI access, low-agency approach with AI access, or a human-only control) and generated creative uses for everyday objects. Contrary to our expectations, ideas did not differ in individual-level quality (overall creativity, originality, and usefulness). However, a preregistered similarity-to-centroid analysis and an exploratory cluster analysis provided convergent evidence of AI-induced homogenization among the low-agency condition. Thus, while AI has enabled greater ideational fluency, our research suggests the degree of agency with which people approach their AI tool has downstream consequences on collective creativity.

Emerging adults (EAs) often struggle with their identity, making them vulnerable to mental health issues.This paper examines how EAs explore their identity through life-story writing, with an eye on how AI might help.Our interview study found that mandatory writing assignments, such as a Statement of Purpose for college applications, often triggered EAs' identity exploration.These writing/identity exploration processes were collaborative between EAs and those closest to them.Collaborations succeeded when both parties had the skills and confidence to discuss EAs' identities with enough intensity and directness, but not so much that it crossed the boundaries of their relationship.When collaborations failed, EAs resorted to consulting AI.These findings offer an alternative perspective to the traditional design of cognitive AI writing assistants, which assumes writers write about their lives proactively and privately.This work suggests that AI assistants might be more effective if they help (1) initiate EAs' identity reflection while moderating its intensity, and (2) serve as a connective tissue among EAs and their support networks.

Health information technologies are transforming how mental healthcare is paid for through value-based care programs, which tie payment to data quantifying care outcomes. But, it is unclear what outcomes data these technologies should store, how to engage users in data collection, and how outcomes data can improve care. Given these challenges, we conducted interviews with 30 U.S.-based mental health clinicians to explore the design space of health information technologies that support outcomes data specification, collection, and use in value-based mental healthcare. Our findings center clinicians' perspectives on aligning outcomes data for payment programs and care; opportunities for health technologies and personal devices to improve data collection; and considerations for using outcomes data to hold stakeholders including clinicians, health insurers, and social services financially accountable in value-based mental healthcare. We conclude with implications for future research designing and developing technologies supporting value-based care across stakeholders involved with mental health service delivery.

What is the potential value and role for AI to facilitate real-time creative discussions? The paper explores principles for Generative-AI based conversational support by investigating how humans – playing the role of an AI agent – generate contextual conversational cues to guide an ideation session. We studied n=42 people (14 triads) brainstorming through a remote meeting design probe that allows a wizard facilitator to oversee the ideation and send text-based cues that appear real-time in the ideator interface. Thematic analysis of conversations, cues and post-hoc reflections by facilitators uncovered focal points, strategies and challenges. Notably, 44% of the cues sent out by the facilitators were either dismissed or ignored because they did not notice the cue update. When ideators did notice cues, certain facilitator strategies impacted the conversation more than others. Based on our analysis, we present design opportunities to improve generative AI-based systems to better support real-time creative collaborations.

The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer's perspective on education and advocacy for ethical designs; and (2) the policymaker's perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting.