# How Do UX Practitioners Communicate AI as a Design Material? Artifacts, Conceptions, and Propositions

arXiv: https://arxiv.org/abs/2305.17389 · HTML: https://mlsanity.com/paper/2305.17389/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2023.

## Built on by (most cited first, 6 of 6)

- [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)
- [On the Regulatory Potential of User Interfaces for AI Agent Governance](https://mlsanity.com/paper/2512.00742/) (2025)
- [Human-Centered AI Product Prototyping with No-Code AutoML: Conceptual Framework, Potentials and Limitations](https://mlsanity.com/paper/2402.07933/) (2024)
- [Exploring the Innovation Opportunities for Pre-trained Models](https://mlsanity.com/paper/2505.15790/) (2025)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)

## Abstract (arXiv)

UX practitioners (UXPs) face novel challenges when working with and communicating artificial intelligence (AI) as a design material. We explore how UXPs communicate AI concepts when given hands-on experience training and experimenting with AI models. To do so, we conducted a task-based design study with 27 UXPs in which they prototyped and created a design presentation for a AI-enabled interface while having access to a simple AI model training tool. Through analyzing UXPs' design presentations and post-activity interviews, we found that although UXPs struggled to clearly communicate some AI concepts, tinkering with AI broadened common ground when communicating with technical stakeholders. UXPs also identified key risks and benefits of AI in their designs, and proposed concrete next steps for both UX and AI work. We conclude with a sensitizing concept and recommendations for design and AI tools to enhance multi-stakeholder communication and collaboration when crafting human-centered AI experiences.