An increasing number of tools now integrate AI support, extending the ability of users—especially novices—to produce creative work. While AI could play various roles within such tools, less is known about how the positioning of AI affects an individual's cognitive processes and sense of agency. To examine this relationship, we built a collaborative whiteboard plugin that integrates an LLM into design templates to facilitate reflective brainstorming activities. We conducted a between-subjects experiment with N=47 participants assigned to one of three versions of AI-support—No-AI, AI input provided incrementally (Co-led) and AI provided all at once (AI-led)—to compare the allocation of cognitive resources. Results show that the positioning of AI scaffolds shifts the underlying cognition: AI-led participants devoted more time to comprehension and synthesis, which yielded more topically diverse problems and solutions. No-AI and Co-led participants spent more time revising content and reported higher confidence in their process.
https://dl.acm.org/doi/10.1145/3706598.3713649
Recent advancements in generative artificial intelligence (AI) are profoundly impacting the broadcasting industry. While generative AI shows promise in supporting broadcasting professionals, its practical workflow integration remains underexplored. In this study, we conducted a user-focused investigation to understand how AI-based content creation support tools are being adopted and perceived in South Korean broadcasting stations. We used the AI Editing Assistant, an AI-powered post-production support tool, as a research probe. Through in-depth interviews with 37 diverse participants—including directors, editors, producers, developers, and executives—we discovered that generative AI significantly enhances production efficiency and unlocks new creative possibilities. However, we identified challenges such as lack of user-centered approach, demanding nature of broadcasting workflows, and professionals' low trust in AI technologies hinders widespread adoption. Based on our findings, we propose implications, considerations, and guidelines for integrating generative AI into broadcasting practices, emphasizing improved multi-stakeholder communication and collaboration for effective and sustainable AI adoption.
https://dl.acm.org/doi/10.1145/3706598.3713532
Large language models are transforming the creative process by offering unprecedented capabilities to algorithmically generate ideas. While these tools can enhance human creativity when people co-create with them, it's unclear how this will impact unassisted human creativity. We conducted two large pre-registered parallel experiments involving 1,100 participants attempting tasks targeting the two core components of creativity, divergent and convergent thinking. We compare the effects of two forms of large language model (LLM) assistance---a standard LLM providing direct answers and a coach-like LLM offering guidance---with a control group receiving no AI assistance, and focus particularly on how all groups perform in a final, unassisted stage. Our findings reveal that while LLM assistance can provide short-term boosts in creativity during assisted tasks, it may inadvertently hinder independent creative performance when users work without assistance, raising concerns about the long-term impact on human creativity and cognition.
https://dl.acm.org/doi/10.1145/3706598.3714198
Concept designers in the entertainment industry create highly detailed, often imaginary environments for movies, games, and TV shows. Their early ideation phase requires intensive research, brainstorming, visual exploration, and combination of various design elements to form cohesive designs. However, existing AI tools focus on image generation from user specifications, lacking support for the unique needs and complexity of concept designers' workflows. Through a formative study with 12 professional designers, we captured their workflows and identified key requirements for AI-assisted ideation tools. Leveraging these insights, we developed AIdeation to support early ideation by brainstorming design concepts with flexible searching and recombination of reference images. A user study with 16 professional designers showed that AIdeation significantly enhanced creativity, ideation efficiency, and satisfaction (all \textit{p}<.01) compared to current tools and workflows. A field study with 4 studios for 1 week provided insights into AIdeation's benefits and limitations in real-world projects. After the completion of the field study, two studios, covering films, television, and games, have continued to use AIdeation in their commercial projects to date, further validating AIdeation's improvement in ideation quality and efficiency.
https://dl.acm.org/doi/10.1145/3706598.3714148
In product design, effective design space exploration (DSE) is crucial for generating high-quality design ideas, requiring designers to possess broad knowledge and balance various constraints. As large-scale models thrive, AI has become an indispensable design collaborator by providing cross-domain knowledge and assistance with complex reasoning. To facilitate collaborative DSE between designers and AI, we frame and advance the design process through the problem-solution co-evolution model and design reasoning methods. A formative study was conducted to identify key strategies for the implementation. Then we developed CoExploreDS, a system that formalizes problems and solutions emerging in the human-AI collaborative design space into nodes. Using four reasoning methods, this system dynamically generates suggestions based on the ongoing design process. User studies confirmed that CoExploreDS significantly improves design quality and the human-AI collaboration experience.
https://dl.acm.org/doi/10.1145/3706598.3713869
With the rise of AI technologies and their growing influence in the screenwriting field, understanding the opportunities and concerns related to AI's role in screenwriting is essential for enhancing human-AI co-creation. Through semi-structured interviews with 23 screenwriters, we explored their creative practices, attitudes, and expectations in collaborating with AI for screenwriting. Based on participants' responses, we identified the key stages in which they commonly integrated AI, including story structure and plot development, screenplay text, goal and idea generation, and dialogue. Then, we examined how different attitudes toward AI integration influence screenwriters' practices across various workflow stages and their broader impact on the industry. Additionally, we categorized their expected assistance using four distinct roles of AI: actor, audience, expert, and executor. Our findings provide insights into AI's impact on screenwriting practices and offer suggestions on how AI can benefit the future of screenwriting.
https://dl.acm.org/doi/10.1145/3706598.3714120
Large Language Models (LLMs) have been widely used to support ideation in the writing process. However, whether generating ideas with the help of LLMs leads to idea fixation or idea expansion is unclear. This study examines how different timings of LLM usage - either at the beginning or after independent ideation - affect people's perceptions and ideation outcomes in a writing task. In a controlled experiment with 60 participants, we found that using LLMs from the beginning reduced the number of original ideas and lowered creative self-efficacy and self-credit, mediated by changes in autonomy and ownership. We discuss the challenges and opportunities associated with using LLMs to assist in idea generation. We propose delaying the use of LLMs to support ideation while considering users' self-efficacy, autonomy, and ownership of the ideation outcomes.
https://dl.acm.org/doi/10.1145/3706598.3713146