Enterprise AI Analysis
Inkspire: Supporting Design Exploration with Generative Al through Analogical Sketching
Authors: David Chuan-En Lin, Hyeonsu B. Kang, Nikolas Martelaro, Aniket Kittur, Yan-Ying Chen, Matthew K. Hong
Abstract: With recent advancements in the capabilities of Text-to-Image (T2I) Al models, product designers have begun experimenting with them in their work. However, T2I models struggle to interpret abstract language and the current user experience of T2I tools can induce design fixation rather than a more iterative, exploratory process. To address these challenges, we developed Inkspire, a sketch-driven tool that supports designers in prototyping product design concepts with analogical inspirations and a complete sketch-to-design-to-sketch feedback loop. To inform the design of Inkspire, we conducted an exchange session with designers and distilled design goals for improving T2I interactions. In a within-subjects study comparing Inkspire to ControlNet, we found that Inkspire supported designers with more inspiration and exploration of design ideas, and improved aspects of the co-creative process by allowing designers to effectively grasp the current state of the AI to guide it towards novel design intentions.
Executive Impact at a Glance
Inkspire significantly enhances design workflows by fostering greater creativity, control, and efficiency in AI-assisted product design.
5.83
Avg. Exploration Score (Inkspire)
6.08
Avg. Usage Experience (Inkspire)
5.58
Avg. Controllability (Inkspire)
17.3
Avg. Strokes per Sketch (Inkspire)
Deep Analysis & Enterprise Applications
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Creativity
Human-AI Collaboration
Workflow & Process
Key Findings
Analogical Sketching
Enhanced Creative Exploration with Inkspire
5.83
Average Exploration Score (Inkspire)
Participants reported significantly higher exploration with Inkspire (μ=5.83, σ=1.27) as compared to the baseline (μ=3.83, σ=1.64), (t(11)=3.94, p<0.01, r=0.77, ds=1.13). Inkspire was found to significantly boost design exploration, enabling designers to ideate more novel concepts compared to traditional T2I tools.
Improved Human-AI Controllability
5.58
Average Controllability Score (Inkspire)
Participants felt significantly higher controllability when using Inkspire (μ=5.58, σ=1.00) as compared to the baseline (μ=4.17, σ=1.19), (t(11)=3.56, p<0.01, r=0.73, ds=1.03). This indicates designers have a stronger sense of guiding the AI's output.
Inkspire's Sketch2Design Pipeline
Enterprise Process Flow
Initial Concept (Subject + Abstract)
→
LLM (Analogical Inspiration)
→
User Sketch Input
→
ControlNet (AI Design Generation)
→
Design2Sketch (Scaffolding Extraction)
The Sketch2Design component helps users brainstorm design concepts and generate product designs through sketching. It begins with user-defined subject and concept, leverages LLMs for analogical inspirations, accepts user sketches, generates designs via ControlNet with dynamic guidance, and extracts foregrounds for a cleaner output.
Comparative Analysis: Inkspire vs. Baseline ControlNet
| Feature | Inkspire (μ) | Baseline (μ) | Impact & Significance |
|---|---|---|---|
| Design Quality | 5.92 | 4.58 | Higher quality, less spread (p=0.06) |
| Usage Experience Satisfaction | 6.08 | 4.33 | Significantly higher satisfaction (p<0.01) |
| Exploration | 5.83 | 3.83 | Significantly higher exploration (p<0.01) |
| Controllability | 5.58 | 4.17 | Significantly higher controllability (p<0.01) |
| Prompting Frequency | 8.50 | 11.9 | Fewer manual prompt edits (Inkspire) |
| Sketch Strokes | 17.3 | 59.8 | Fewer total strokes (Inkspire) |
Inkspire consistently outperformed the baseline ControlNet in several key areas, promoting a more iterative and collaborative design process. Designers reported higher satisfaction and a greater sense of control and inspiration, while engaging in fewer prompt edits and sketch strokes, indicative of a more efficient workflow.
Diverse Analogical Inspiration Exploration
Analogical Inspiration Diversity
Participants demonstrated a broad exploration of analogical inspirations (μ=4.58, σ=2.43), distributed across Nature (μ=2.08), Architecture (μ=1.58), and Fashion (μ=0.917) categories. While Nature-based inspirations were explored most frequently, Architecture was the most common final choice, indicating its utility for product design. The ability to switch between categories and draw inspiration from varied sources, such as 'Nature Architecture' or 'Nature Fashion' transitions, suggests a flexible approach to concept generation. This rich exploration helped designers break fixation and generate novel designs by considering concepts beyond their primary domain.
Impact: This rich exploration helped designers break fixation and generate novel designs by considering concepts beyond their primary domain.
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Your AI Implementation Roadmap
Our proven methodology ensures a seamless integration of AI, maximizing your ROI and minimizing disruption. Here's how we'll get you started.
Phase 1: Discovery & Strategy
Collaborative workshops to understand your current design workflows, identify key pain points, and define AI integration objectives. We'll assess your infrastructure and data readiness.
Phase 2: Pilot Program & Customization
Implement a tailored Inkspire pilot with a select design team. Gather feedback, fine-tune AI models for your specific design language, and integrate with existing tools.
Phase 3: Scaling & Training
Roll out Inkspire across your broader design organization. Comprehensive training for all users and continuous support to ensure adoption and proficiency.
Phase 4: Optimization & Future-Proofing
Ongoing performance monitoring, iterative improvements, and strategic planning for future AI advancements to maintain your competitive edge in design innovation.
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