Researchers at Swansea University have conducted one of the largest studies to date on human-AI creative collaboration, involving over 800 participants designing virtual cars. The study found that when participants were shown AI-generated design galleries — created using a method called MAP-Elites — they spent more time on tasks, produced better designs, and felt more creatively engaged. Crucially, the galleries included a wide spectrum of ideas, from highly effective to intentionally flawed, which helped participants break free from initial assumptions and explore broader possibilities.

The research, published in ACM Transactions on Interactive Intelligent Systems, also challenges how AI design tools are typically evaluated. Standard metrics tend to focus on surface-level behaviors like click rates and copying frequency, but the Swansea team argues these miss the deeper cognitive and emotional effects AI has on users. Lead author Dr. Sean Walton emphasized that diversity in AI output — including imperfect ideas — was key to preventing early fixation and encouraging creative risk-taking. As AI becomes embedded in fields from architecture to game design, the study suggests the real question isn't what AI can do alone, but how it can enhance human thinking and collaboration.

A Reddit user in the r/OpenAI community poses an open question to creative professionals about how they are actually integrating AI into their workflows. The post acknowledges the persistent stigma around AI in design, art, and creative work, and asks whether people are using it for tasks like brainstorming, references, copywriting, automating repetitive tasks, coding help, mood boards, or ideation — or whether most professionals are avoiding it due to reputation concerns.

The post is framed as a genuine inquiry rather than a debate prompt, seeking honest, real-world examples of AI adoption among creatives. Unfortunately, the saved page does not include any of the community responses, so only the original question is captured here.

Contra shares results from a benchmark test pitting four leading AI models against evaluations by real professional designers. The models tested include Anthropic's Claude Opus 4.6, Google's Gemini 3.1 Pro, OpenAI's ChatGPT 5.3 Codex, and Alibaba's Qwen 3.5. The post teases that the results "might surprise you" and links to an accompanying video or detailed breakdown.

This comparison is notable for its focus on design-oriented tasks evaluated by working professionals rather than standard automated benchmarks, potentially offering more practical insight into how these AI models perform in creative and professional workflows.