# Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools

arXiv: https://arxiv.org/abs/2303.00192 · HTML: https://mlsanity.com/paper/2303.00192/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 110 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation](https://mlsanity.com/paper/2003.06988/) (2020)
- [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022)
- [Teaching Humans When To Defer to a Classifier via Exemplars](https://mlsanity.com/paper/2111.11297/) (2021)
- [It Takes Two to Tango: Towards Theory of AI's Mind](https://mlsanity.com/paper/1704.00717/) (2017)
- [SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows](https://mlsanity.com/paper/2007.15065/) (2020)
- [Designing Composites with Target Effective Young's Modulus using Reinforcement Learning](https://mlsanity.com/paper/2110.05260/) (2021)
- [Towards a Learner-Centered Explainable AI: Lessons from the learning sciences](https://mlsanity.com/paper/2212.05588/) (2022)
- [Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment](https://mlsanity.com/paper/2111.02626/) (2021)

## Built on by (most cited first, 17 of 17)

- [Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation](https://mlsanity.com/paper/2310.12953/) (2023)
- [Design Principles for Generative AI Applications](https://mlsanity.com/paper/2401.14484/) (2024)
- [User Experience Design Professionals' Perceptions of Generative Artificial Intelligence](https://mlsanity.com/paper/2309.15237/) (2023)
- [Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Models](https://mlsanity.com/paper/2310.08574/) (2023)
- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [Intent Tagging: Exploring Micro-Prompting Interactions for Supporting Granular Human-GenAI Co-Creation Workflows](https://mlsanity.com/paper/2502.18737/) (2025)
- [Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation](https://mlsanity.com/paper/2506.12879/) (2025)
- [Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information](https://mlsanity.com/paper/2401.04729/) (2024)
- [What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design](https://mlsanity.com/paper/2406.13436/) (2024)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [Supervising Ralph Wiggum: Exploring a Metacognitive Co-Regulation Agentic AI Loop for Engineering Design](https://mlsanity.com/paper/2603.24768/) (2026)
- [Enabling Generative Design Tools with LLM Agents for Mechanical Computation Devices: A Case Study](https://mlsanity.com/paper/2405.17837/) (2024)
- [AI That Helps Us Help Each Other: A Proactive System for Scaffolding Mentor-Novice Collaboration in Entrepreneurship Coaching](https://mlsanity.com/paper/2508.11052/) (2025)
- [Human Control Is the Anchor, Not the Answer: Early Divergence of Oversight in Agentic AI Communities](https://mlsanity.com/paper/2602.09286/) (2026)
- [AwesomeMeta+: A Mixed-Prototyping Meta-Learning System Supporting AI Application Design Anywhere](https://mlsanity.com/paper/2304.12921/) (2023)
- [No Pixel Left Behind: Filling Gaps in Anime Colorization](https://mlsanity.com/paper/2609.00800/) (2026)
- [Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison](https://mlsanity.com/paper/2610.13082/) (2026)

## Abstract (arXiv)

AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design tasks. These tools take on more agentic roles than traditional computer-aided design tools and are often portrayed as "co-creators." Yet, working effectively with such systems requires different skills than working with complex CAD tools alone. To date, we know little about how engineering designers learn to work with AI-based design tools. In this study, we observed trained designers as they learned to work with two AI-based tools on a realistic design task. We find that designers face many challenges in learning to effectively co-create with current systems, including challenges in understanding and adjusting AI outputs and in communicating their design goals. Based on our findings, we highlight several design opportunities to better support designer-AI co-creation.