The growing popularity of AI writing assistants creates exciting opportunities to support diverse writers. This study examines how personality shapes expectations for AI writing companions and how personality-informed design can enhance human–AI teaming in writing. Through exploratory co-design workshops with 24 writers representing different personality profiles, we elicited values and design ideas for AI writing companions spanning functionality, interaction dynamics, and visual representation. These insights informed two contrasting prototypes reflecting distinct writing orientations, used as design provocations in review-and-refinement workshops with eight participants to prompt reflection on fit, priorities, and writing practices. Our findings reveal both shared foundational needs across writers and meaningful personality-driven preferences that influence how writers engage with AI. This work underscores the importance of team matching in human-AI collaboration and demonstrates how aligning AI companions with individual cognitive and interpersonal needs can improve engagement and perceived collaboration effectiveness.

Most AI systems today are designed to manage tasks and execute predefined steps. This makes them effective for process coordination but limited in their ability to engage in joint problem-solving with humans or contribute new ideas. We introduce MultiColleagues, a multi-agent conversational system that shows how AI agents can act as colleagues by conversing with each other, sharing new ideas, and actively involving users in collaborative ideation processes. In a within-subjects study with 20 participants, we compared MultiColleagues to a single-agent baseline. Results show that MultiColleagues fostered stronger perceived social presence, and participants rated their outcomes as higher in quality and novelty, with more elaboration during ideation. These findings demonstrate the potential of AI agents to move beyond process partners toward colleagues that share intent, strengthen group dynamics, and collaborate with humans to advance ideas.

Knowing when to joke-and when not to-is a subtle skill often missing in large language models (LLMs).This study examines how well LLMs generate and recognize humor in emotionally sensitive, support-oriented conversations.We introduce two targeted datasets: one for evaluating humor generation across distinct styles, and another for testing humor and speaker role recognition in human-written supportive statements.Using GPT-4o, LLaMA3, and Gemini 1.5, we assess humor style alignment, emotional appropriateness, and role sensitivity.While models produce fluent and stylistically varied humor, they often struggle with contextual nuance and role interpretation.GPT-4o consistently performs best in tone alignment and emotional fit, but subtle humor types remain challenging across models.These results highlight current limitations in LLMs' pragmatic and relational understanding, underscoring the importance of human oversight in humor-sensitive applications.

Professional designers often construct and explore conceptual representations (e.g.: design spaces) to help them reason about complex design situations and consider potential design pitfalls. However, it is often challenging, even for professional designers, to exhaustively consider the many pitfalls that might result from design activity. We present CausalMapper, a mixed-initiative system, that leverages a large language model (LLM) and a causal map representation to teach design students how to reason about the relationships between problems and solutions. Where creativity support tools often focus on ideating creative solutions, our mixed-initiative approach focuses on ideating ecosystems of solutions that holistically address a set of related problems. By leveraging the generative creativity of LLMs, designers are inspired to consider solutions and potential consequences that emerge when solutions are adopted. At the same time, leveraging the designers’ domain knowledge to account for and correct the biases inherent in LLMs. Through a case study, we demonstrate the functionality of this mixed-initiative system. The goal of this demo is to present a creativity support tool that is intended to teach design students to think more systematically by generating ideas that challenge their thinking rather just augmenting their creative potential.

Clinical decision support tools (DSTs), powered by Artificial Intelligence (AI), promise to improve clinicians’ diagnostic and treatment decision-making. However, no AI model is always correct. DSTs must enable clinicians to validate each AI suggestion, convincing them to take the correct suggestions while rejecting its errors. While prior work often tried to do so by explaining AI’s inner workings or performance, we chose a different approach: We investigated how clinicians validated each other’s suggestions in practice (often by referencing scientific literature) and designed a new DST that embraces these naturalistic interactions. This design uses GPT-3 to draw literature evidence that shows the AI suggestions’ robustness and applicability (or the lack thereof). A prototyping study with clinicians from three disease areas proved this approach promising. Clinicians’ interactions with the prototype also revealed new design and research opportunities around (1) harnessing the complementary strengths of literature-based and predictive decision supports; (2) mitigating risks of de-skilling clinicians; and (3) offering low-data decision support with literature.

Problem framing—the process of defining a problem—has been described by many researchers and designers as the crux of the design process. However, novice designers struggle with problem framing. To better understand this process and the potential for scaffolding, we conducted two studies. In the first study, we analyzed 41 problem statements from an introductory design course and found that novices often omit key information, like the primary stakeholder or the obstacles they face. To get novices to reflect on and include necessary design information, we created a tool called ProbLib that cues novices to explicitly reflect on aspects of the problem such as the stakeholders. To evaluate this approach, we conducted a between-subjects study (N=73) to compare ProbLib with an unstructured open text form. We found that participants using ProbLib wrote higher quality statements, included more information, and were more confident about specifying design needs. We observed creative behaviors such as brainstorming and analogical reasoning.