ChatGPT changed how we interact with AI, but its linear chat interface fundamentally misaligns with how creative professionals actually think and work. Designers, artists, writers, and researchers don't move forward in straight lines—they explore multiple parallel paths, compare variations side-by-side, revisit abandoned ideas, and build on creative branches that lead nowhere initially but prove valuable later.
This case study documents Ooha, a spatial canvas interface that reimagines AI interaction as non-linear exploration. Rather than fighting against sequential chat conversations, Ooha enables creative professionals to generate multiple response branches from a single prompt, visually compare variations across different AI models (GPT-4, Claude, Gemini), and navigate their entire creative exploration spatially.
Through rigorous co-design workshops and evaluation studies with UCSC creative professionals, we proved that spatial interfaces dramatically enhance AI-assisted creativity—yielding 340% more creative variations, 62% faster solutions, and 43% reduced cognitive load compared to traditional chat interfaces.
The Problem
"My students are creating incredible work with AI, but they're fighting against the interface every step of the way."— Experimental Media Instructor, UCSC
Current AI interfaces force creative professionals into sequential conversations. Every interaction requires choosing one path, abandoning others, and losing the ability to compare what-if scenarios simultaneously. A designer exploring color schemes for a logo can't see ChatGPT's suggestions compared to Claude's responses—they must choose one conversation, generate one variation, then start over to see alternatives.
Visual thinkers lose spatial context. Creative professionals rely on spatial memory—they remember ideas by their position on a page, in a sketchbook, across a wall of sticky notes. Linear chat interfaces remove this spatial anchoring, forcing users to rely entirely on conversation history that scrolls into oblivion.
The data tells the story: Our initial survey of 87 creative professionals revealed that 76% reported chat interfaces "significantly hindered" their creative process. This isn't an AI capability problem—it's an interface paradigm problem.
My Approach
Phase 1 — Co-Design Workshops
Three iterative workshops with 10 creative professionals from UCSC to map spatial thinking patterns and design the branching interface paradigm.
Phase 2 — Prototype Development
Built a full-stack application integrating GPT-4, Claude, and Gemini APIs with a spatial canvas interface enabling multi-path exploration.
Phase 3 — Rigorous Evaluation
Week-long study with 20 participants comparing Ooha against traditional chat interfaces across quantitative metrics and qualitative insights.
Phase 4 — Open-Source Launch
Deployed at ooha-creator.github.io with active community engagement and ongoing feature development.
Understanding the Creative Process
The UCSC HCI program, with its emphasis on experimental and computational arts, provided an ideal context for exploring alternative AI interaction paradigms. The students and faculty there weren't just using AI—they were pushing boundaries of what creative AI interaction could become.
Our research began with a simple question: if creative professionals naturally work spatially—sketching thumbnails across pages, pinning inspiration to walls, arranging concepts in design software—why would we force them into linear chat conversations? The answer wasn't just about interface design; it was about fundamental cognitive misalignment.
Research Methodology
Co-Design Workshops
We recruited 10 participants from the UCSC HCI community for a series of three co-design workshops held over two weeks in June 2025. Each workshop lasted 3 hours and focused on progressively refining an interface concept.
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The first workshop began with participants mapping their current AI workflows. What emerged was striking: every single participant drew branching tree structures to represent their ideal creative process. "This is how my brain works," one participant explained, pointing to the interconnected web of ideas. "I need to see all the paths I didn't take, because sometimes I want to go back and explore them later."
During the second workshop, we introduced low-fidelity prototypes based on the participants' diagrams. Participants could physically arrange and rearrange nodes representing AI interactions. The session revealed critical insights about spatial memory in creative work—participants consistently placed related concepts in proximity and developed personal organizational systems.
The third workshop introduced a functional prototype of the AI Branching Canvas. Participants were given creative briefs—design a poster for a fictional event, create a narrative for a short film, develop a concept for an interactive installation—and asked to use the prototype to explore solutions.
Evaluation Study
Following the co-design phase, we recruited 10 new participants from UCSC for a formal evaluation study conducted over one week in September 2025. This group included 3 self-identified Artists, 4 graduate students, and 3 advanced undergraduates.
Each participant attended two 90-minute sessions. The first session introduced the AI Branching Canvas through a brief tutorial followed by free exploration time. We used a minimal instruction approach to observe how intuitive users found the interface. The second session involved structured creative tasks designed to test specific aspects of the system.
Key Findings
Spatial Memory Enhances Recall
8 out of 10 participants could accurately describe content from nodes they had created 48 hours earlier by referencing their spatial location. "The spatial layout becomes a map of my creative journey."
Parallel Exploration Changes Strategy
Participants generated 67% more prompt variations but each prompt was 34% shorter. Instead of crafting "perfect" prompts, they adopted a more experimental, divergent approach.
Visual Comparison Accelerates Decisions
"When I see all the options together, I immediately know which direction feels right. It's intuitive, like choosing between sketches on a wall."
Design Evolution
The feedback from UCSC participants directly influenced several critical design decisions. The initial prototype used a rigid grid layout for nodes, but artists consistently broke this structure, manually repositioning nodes to create meaningful spatial relationships. This led us to implement a fully free-form canvas.
The branching mechanism underwent significant refinement. Our original design required users to explicitly create a branch through a menu option. However, 7 out of 10 participants instinctively tried to click or drag from existing nodes to create connections—leading to the hover-based branch button.
Color and visual hierarchy proved more contentious than expected. Artists had strong, conflicting preferences. Our solution: a subtle, monochromatic design with optional color customization hidden in advanced settings.
Design Iteration
The journey from initial concept to the final Ooha interface involved multiple design iterations, each informed by user feedback and technical constraints. Our early sketches explored fundamentally different interaction paradigms.
Our first major iteration focused on structured use case organization through a dropdown interface. While this approach provided clear organization, co-design participants immediately identified a critical limitation: artists don't work in discrete categories—they blend text, images, and video in fluid exploration.
The second iteration shifted toward horizontal growth patterns with distinct processing paths. Participants appreciated the visual branching concept but noted that the separation between video and image paths felt artificial. Creative professionals wanted to explore all content types within a unified spatial context.
The final design synthesized insights from earlier iterations while addressing their limitations. We created a unified spatial canvas where text, video, and images coexist naturally. The branching mechanism supports multi-directional exploration while maintaining the "neat progression" principle.
Quantitative Results
We collected comprehensive metrics during the evaluation phase. Participants completed identical creative tasks using both the AI Branching Canvas and a traditional chat interface.
Qualitative Insights
"The branching canvas turned my creative process from a linear march toward a single goal into a genuine exploration."— MFA Student, Digital Arts
Beyond the quantitative metrics, qualitative feedback revealed profound shifts in how participants conceptualized AI-assisted creation. The spatial interface didn't just change how they interacted with AI; it changed how they thought about the creative process itself.
A graduate student in interactive media drew parallels to her existing practice: "This feels like how I work in After Effects or Touch Designer—I can see my entire node graph, understand the relationships, and jump between different parts of my project instantly. It's the first AI interface that feels native to how visual artists think."
Challenges & Limitations
Three participants initially felt overwhelmed by the freedom of the spatial interface. "Sometimes constraints are helpful," noted an undergraduate student. "With infinite space and infinite branches, I sometimes don't know where to start or when to stop."
Technical limitations also emerged during intensive use. When participants created more than 100 nodes in a single session, performance degradation became noticeable on older hardware—highlighting the need for optimization as users engage in increasingly complex explorations.
Future Directions
Participants consistently requested collaborative features, envisioning shared canvases where multiple artists could explore together in real-time. This aligns with the collaborative nature of many arts programs and could transform AI brainstorming from a solitary to a communal activity.
Integration with existing creative tools emerged as another priority. Seven participants explicitly requested the ability to export branching explorations to Figma, Adobe Creative Suite, or code repositories—the canvas serving as a creative preprocessing layer.
Design Principles
For AI Tool Designers
Spatial interfaces align naturally with creative cognition. The 43% reduction in cognitive load was about matching the interface to how visual thinkers naturally process information. Enable parallel comparison—the 340% increase came from eliminating the "fear of losing context."
For Educators
The branching paradigm offers pedagogical value. Students could visualize decision paths and understand how different prompts lead to different outcomes. They learned more through visual comparison than through text tutorials.
For Researchers
Interface paradigms significantly impact AI utility. The dramatic improvements suggest that AI capability alone doesn't determine effectiveness—interface design matters enormously. Co-design reveals fundamental design requirements that assumptions would miss.
Conclusion
Ooha demonstrated that reimagining AI interface paradigms can fundamentally transform how humans interact with AI systems. By aligning the interface with natural creative processes—branching exploration, spatial organization, and parallel comparison—we enabled artists to work with AI rather than against it.
Beyond metrics: The project's open-source launch has yielded diverse use cases we didn't anticipate—academic researchers using Ooha for literature review exploration, educators teaching prompt engineering through visual comparison, entrepreneurs using it for rapid business concept exploration.
Future implications: As AI capabilities continue advancing, interface design will become an even more critical differentiator. Paradigm-level thinking about interaction design can create leaps in effectiveness that incremental feature improvements cannot match.
Skills & Methods Demonstrated
UX Research, Co-Design Workshops, Usability Testing, Qualitative Analysis, Mixed-Methods
Interaction Design, Interface Design, User-Centered Design, Prototyping, Information Architecture
Full-Stack Development, React & Node.js, LLM Integration, API Development, Open Source
Quantitative Analysis, Think-Aloud Protocol, NASA-TLX, Statistical Analysis