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Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers' reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. Motivated by these challenges, we envision novel metacognitive support agents that assist designers in working more reflectively with GenAI. To explore this vision, we conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies. We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies. Based on these findings, we discuss opportunities and tradeoffs of metacognitive support agents and considerations for future AI-based design tools.
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This content will become publicly available on April 25, 2026
Productive vs. Reflective: How Different Ways of Integrating AI into Design Workflows Affect Cognition and Motivation
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.
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- Award ID(s):
- 2009003
- PAR ID:
- 10654074
- Publisher / Repository:
- ACM
- Date Published:
- Page Range / eLocation ID:
- 1 to 15
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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