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Supporting Designers in Learning to Co-create with AI for Complex Computational Design Tasks: 2118924

Principal Investigator: Nikolas Martelaro
CoPrincipal Investigator(s): Lining Yao, Kenneth Holstein
Organization: Carnegie-Mellon University
NSF Award Information: Supporting Designers in Learning to Co-create with AI for Complex Computational Design Tasks
As modern design tasks grow increasingly challenging, artificially intelligent (AI) design tools have the potential to provide new support to designers and engineers. This project will work to enable a future of computational co-creation, in which humans and AI collaborate and learn from each other to create new designs. The project addresses a vital national need: to prepare the emerging workforce in design, engineering, and manufacturing to better solve the complex problems of today by collaborating with AI design tools. In practice, co-creation with AI presents a significant learning curve for designers. The research team will study how people learn to collaborate with AI on real-world design tasks. The team will continuously build and test new ways for AI and designers to learn and interact through conversational and graphical interfaces. Success in this project is expected to advance our understanding of how people learn to collaborate with AI on complex computational co-creation tasks. This project is expected to lead to new training techniques and software design guidelines for AI-enabled design tools and other human-AI co-creative tasks. The research team will share their new interface designs and strategies to support other researchers in studying human-AI co-creation and to support companies in developing new AI-enabled design tools. The team will also incorporate the developed tools and research knowledge into classes for university and high school students, introducing them to human-AI collaboration and preparing them to work with and develop such systems in the future.

The research team will conduct iterative, human-centered design research to advance our understanding of how people learn to collaborate with AI on complex design tasks, while using widely available AI design tools, in the context of designing actively transforming structures. This is a complex, emerging manufacturing task. Steps in the research include: (1) Conduct a series of think-aloud activities to investigate how designers (try to) learn to collaborate with currently available AI design tools. The findings from these activities are expected to provide understandings of current challenges in human-AI design collaboration and will surface the strategies and mental models that people use when learning to collaborate with AI. (2) Prototype novel interface features to advance human–AI co-creation and learning. These will include a range of interactions and interface modalities, building upon theories of effective conversational exchange and supporting controlling actions, delegating actions, and negotiating goals and means. (3) Evaluate these interfaces and interactions to see how well they support designers in learning to interact and productively work with the AI. (4) Use a mix of conversation analysis and multimodal observations to understand how the interface prototypes influence human-AI co-creation. The research is expected to produce new approaches to help researchers study and design human-AI co-creation, including: (a) measures for assessing learning and collaboration in the context of human-AI co-creative tasks; (b) prototyping methods for human-AI collaborative systems; (c) interaction design guidelines for onboarding and supporting designers in human-AI co-creation; and (d) new theory about how conversational interfaces can support designers in learning to co-create with AI.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

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