Generative AI has greatly transformed creative work in various domains, such as screenwriting. To understand this transformation, prior research often focused on capturing a snapshot of human-AI co-creation practice at a specific moment, with less attention to how humans mobilize, regulate, and reflect to form the practice gradually. Motivated by Bandura's theory of human agency, we conducted a two-week study with 19 professional screenwriters to investigate how they embraced AI in their creation process. Our findings revealed that screenwriters not only mindfully planned, foresaw, and responded to AI usage, but, more importantly, through reflections on practice, they developed themselves and human-AI co-creation paradigms, such as cognition, strategies, and workflows. They also expressed various expectations for how future AI should better support their agency. Based on our findings, we conclude this paper with extensive discussion and actionable suggestions to screenwriters, tool developers, and researchers for sustainable human-AI co-creation.
Critics have argued that mobile usability has largely been optimized, and that only incremental gains are possible. We set out to explore if the newest generation of design systems, which promote greater flexibility and a return to design basics, could produce substantially more usable designs while maintaining or increasing aesthetic judgments. Through a study with 48 diverse participants completing tasks in 10 different applications, we found that in designs created following Material 3 Expressive guidelines, users fixated on the correct screen element for a task 33% faster, completed tasks 20% faster, and rated experiences more positively compared to versions designed using the previous Material design system. These improvements in performance and aesthetic ratings challenge the premise of a usability plateau and show that mobile usability has not peaked. We illustrate specific opportunities to make mobile experiences more usable by returning to design fundamentals while highlighting risks of added flexibility.
Visual designers often seek inspiration from Chinese paintings when tasked with creating Chinese-style illustrations, posters, etc. Our formative study (N=10) reveals that during ideation, designers learn the cultural symbols, emotions, compositions, and styles in Chinese paintings but face challenges in searching, analyzing, and integrating these dimensions. This paper leverages multi-modal large models to annotate the value of each dimension in 16,315 Chinese paintings, built on which we propose InkIdeator, an ideation support system for Chinese-style visual designs. InkIdeator suggests cultural symbols associated with the task theme, provides dimensional keywords to help analyze Chinese paintings, and generates visual examples integrating user-selected keywords. Our within-subjects study (N=12) using a baseline system without extracted dimensional keywords, along with two extended use cases by Chinese painters, indicates InkIdeator’s effectiveness in creative ideation support, helping users efficiently explore cultural dimensions in Chinese paintings and visualize their ideas. We discuss implications for supporting culture-related visual design ideation with generative AI.
Designers often regard vagueness as an essential aspect of creative work, as it fosters diverse interpretations and helps prevent fixation. Although large language models (LLMs) are increasingly viewed as a promising creative partner, designers struggle to productively incorporate vagueness into AI-supported workflows. To address this challenge, we present QuerySwitch, an interactive prototype that enables fashion designers to manage vagueness by flexibly switching between two distinct query-output modes. Findings from a user study show that QuerySwitch helps fashion designers balance vagueness, enhances the usability of LLMs in design tasks, and promotes creative exploration. This work contributes to HCI by (1) foregrounding a critical construct in human–AI collaboration, (2) demonstrating how interaction mechanisms can scaffold designer agency in LLMs use, and (3) articulating design principles—structuring exploration and preserving key query formulations—that extend to creativity-driven domains.
This study examines the psychological mechanisms that enable sustainable human–AI collaboration in creative tasks. Drawing on the CASA paradigm, we frame generative AI as a social collaborator and integrate UTAUT, psychological ownership theory, and self-determination theory to explain users’ continued engagement with AI tools. We test how AI performance expectancy influences psychological ownership, collaboration satisfaction, and continuance intention, and whether these mechanisms vary by creative context (pure vs. work-related) or collaboration type (human-led vs. AI-led). Results show that performance expectancy enhances ownership, satisfaction, and continuance intention, with ownership and satisfaction further reinforcing continued use. However, in AI-led collaboration, its positive effect on satisfaction is weakened, while creative context shows no significant differences, suggesting that core psychological processes generalize across creative purposes. This study extends UTAUT by incorporating psychological mechanisms into human–AI collaboration and provides a theoretical basis for sustainable use of generative AI.
In digital knowledge work, flow promises not just productivity; it offers a pathway to well-being. Yet despite decades of flow research in HCI, we know little about how to design digital interventions that support it. In this work, we foreground lived interventions — everyday practices workers already use to foster flow — to uncover overlooked opportunities and chart new directions for digital intervention design. Specifically, we report findings from two studies: (1) a reflexive thematic analysis of open-ended survey responses (n = 160), surfacing 38 lived interventions across four categories: environment, organization, task shaping, and personal readiness; and (2) a quantitative online survey (n = 121) that validates this repertoire, identifies which interventions are broadly endorsed versus polarizing, and elicits visions of technological support. We contribute empirical insights into how digital workers cultivate flow, situate these lived interventions within existing literature, and derive design opportunities for future digital flow interventions.
Expressive digital drawing requires nuanced motor control, subtle variations in pressure, velocity, and rhythm that convey affect and style. While experts develop this embodied fluency through years of practice, novices struggle to produce marks that match their intentions, creating a gap between vision and execution. We propose motor-mediated creativity: treating motor training as integral to digital expression. Our system, {\system}, instantiates this through structured practice of expressive primitives, expert-referenced feedback, and ideation prompts that encourage exploration.
We report a two-stage investigation. A formative study characterized: (a) novice challenges in motor fluency, (b) examined how different feedback types, including corrective feedback, helped participants understand their mistakes, (c) how prompts, generic or embodied, support engagement with abstract expressive content. A controlled evaluation then linked fluency gains to subjective and expert ratings of expressiveness. Together, our findings show that scaffolding motor skills is a viable strategy for enhancing expressive agency in digital drawing.