IdeaBlocks: Expressing and Reusing Exploratory Intents for Design Exploration with Generative AI

2025-07-29

AI Summary

Designers face three key challenges in generative AI–assisted design exploration: difficulty articulating open-ended intents, lack of continuity in exploration processes, and insufficient support for creative reuse and iterative refinement. To address these, we propose IdeaBlocks—a modular design exploration framework that structures exploratory intent into reusable, composable Exploration Blocks. These blocks enable branching, backtracking, and cross-context reuse, while a block-based visual interface and structured input mechanism facilitate explicit intent modeling and dynamic reconfiguration. A user study demonstrates that IdeaBlocks increases image generation output by 112.8%, improves visual diversity by 12.5%, and yields more iterative and coherent exploration trajectories. This work introduces the first explicit modularization of the design exploration process, establishing a novel interaction paradigm for generative AI design tools—one that supports nonlinear, sustainable creative evolution.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Generative AI opens new possibilities for design exploration by rapidly generating images aligned with user goals. However, our formative study (N=7) revealed three key limitations hindering designers' broad and efficient exploration when interacting with these models. These include difficulty expressing open-ended exploratory intent, lack of continuity in exploration, and limited support for reusing or iterating on previous ideas. We propose IdeaBlocks, where users can express their exploratory intents to generative AI with structured input and modularize them into Exploration Blocks. These blocks can be chained for continuous, non-linear exploration and reused across contexts, enabling broad exploration without losing creative momentum. Our user study with 12 designers showed that participants using IdeaBlocks explored 112.8% more images with 12.5% greater visual diversity than the baseline. They also developed ideas in more iterative and continuous patterns, such as branching, chaining, and revisiting ideas. We discuss design implications for future tools to better balance divergent and convergent support during different phases of exploration, and to capture and leverage exploratory intents more effectively.

Problem

• Difficulty expressing open-ended exploratory intent
• Lack of continuity in design exploration
• Limited support for reusing previous ideas

Innovation

• Structured input for exploratory intents
• Modular Exploration Blocks for reuse
• Chaining blocks for continuous exploration