Study
User-Centred DesignRecentModerate effect

Hands-on AI prototyping fosters clearer communication of AI concepts between UX practitioners and technical stakeholders.

Direct experience with AI model training and experimentation allows UX practitioners to develop a more concrete understanding and effective language for discussing AI's capabilities and limitations with technical teams.

Academic Publication · 2023

01

Key Findings

  • 01Tinkering with AI models broadened common ground for communication with technical stakeholders.
  • 02UX practitioners identified key risks and benefits of AI in their designs.
  • 03UX practitioners proposed concrete next steps for both UX and AI work.
02

Application

Design takeaway

Integrate AI prototyping tools into the design workflow to build a shared understanding and vocabulary for AI-driven features with technical collaborators.

How to apply

Provide designers with access to user-friendly AI development environments or sandboxes to experiment with AI functionalities relevant to their design projects.

Project actions

  • 01If your design project involves AI, try to find ways to interact with or simulate the AI's behavior, even if it's a simplified version.
  • 02Document how your understanding of the AI evolved as you experimented with it.
03

Method & Evidence

AimHow do UX practitioners communicate AI as a design material, and how can this communication be improved through hands-on experience?
MethodTask-based design study with qualitative analysis of design presentations and interviews.
ProcedureUX practitioners were given access to an AI model training tool to prototype and present an AI-enabled interface. Their design presentations and subsequent interviews were analyzed.
Sample27 participants
ContextUser Experience (UX) design practice involving Artificial Intelligence (AI) interfaces.

Variables

IVHands-on experience with AI model training and experimentation.
DVClarity of AI concept communication, identification of AI risks/benefits, common ground with technical stakeholders.
CVType of AI model training tool, design task, participant's prior AI knowledge (potentially).
04

Strengths & Limitations

Strengths

  • +Directly addresses a contemporary challenge in design practice.
  • +Employs a practical, task-based approach to simulate real-world design scenarios.
  • +Analyzes both design outputs and participant reflections.

Limitations

It can be difficult to access real AI models or training tools for a design project. The complexity of AI can make it hard for designers to fully grasp its nuances.

Reliability & validity

The study's validity is supported by the qualitative depth of interviews and analysis of design artifacts. Reliability could be enhanced by standardizing the AI training tool and task more rigorously across participants.

Think critically

To what extent does the 'simplification' of AI tools in a design study accurately reflect the complexities designers face with real-world AI implementations, and how might this impact the generalizability of the findings?

05

Design Principles

"Experiential learning with AI technologies enhances communication and collaboration in human-centered AI design."

In an era of increasingly complex AI-driven products, bridging the communication gap between design and technical disciplines is crucial for successful product development. This research highlights a practical method for designers to gain the necessary understanding to advocate for user needs within AI development processes.

06

What This Means for Your Design

When designers get to play with AI tools themselves, they can talk about AI features with engineers much more easily and understand the good and bad points better.

How to use in your project

  • 1.Reference this study when discussing the challenges of communicating AI concepts in your design project and how your approach aimed to overcome them.
  • 2.Use the findings to justify the inclusion of prototyping or simulation phases for AI components in your design process.
07

Add to My Project

08

Quick Cite

(2023). How Do UX Practitioners Communicate AI as a Design Material? Artifacts, Conceptions, and Propositions. Academic Publication. https://doi.org/10.1145/3563657.3596101 Retrieved from https://designdex.org/study/4c51f372-be13-4183-8f77-5d14dec18134/hands-on-ai-prototyping-fosters-clearer-communication-of-ai-concepts-between-ux-practitioners-and-technical-stakeholders

Paragraph starter

This research indicates that hands-on experience with AI prototyping significantly improves UX practitioners' ability to communicate AI concepts and collaborate effectively with technical stakeholders. By engaging directly with AI models, designers can develop a shared understanding of AI's capabilities, risks, and benefits, leading to more cohesive and user-centered AI-driven designs.

09

Source

Academic Publication

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

journal · 2023

View source

Questions about this research

What does the research say about hands-on ai prototyping fosters clearer communication of ai concepts between ux practitioners and technical stakeholders?
Integrate AI prototyping tools into the design workflow to build a shared understanding and vocabulary for AI-driven features with technical collaborators. Evidence: Academic Publication (2023).
Why does "Hands-on AI prototyping fosters clearer communication of AI concepts between UX practitioners and technical stakeholders." matter for design?
In an era of increasingly complex AI-driven products, bridging the communication gap between design and technical disciplines is crucial for successful product development. This research highlights a practical method for designers to gain the necessary understanding to advocate for user needs within AI development processes.
How can designers apply this research?
Integrate AI prototyping tools into the design workflow to build a shared understanding and vocabulary for AI-driven features with technical collaborators.
What were the main findings?
Tinkering with AI models broadened common ground for communication with technical stakeholders.. UX practitioners identified key risks and benefits of AI in their designs.. UX practitioners proposed concrete next steps for both UX and AI work.
What research method was used?
Task-based design study with qualitative analysis of design presentations and interviews. with 27 participants.
How strong is the evidence?
Evidence strength is rated Moderate effect, based on a 2023 journal from Academic Publication.
What should I do differently in my next project?
Provide designers with access to user-friendly AI development environments or sandboxes to experiment with AI functionalities relevant to their design projects.
What are the limitations?
The study used a simplified AI model training tool, and the findings may not generalize to more complex AI systems. The context was specific to UX practitioners.
Is there evidence that technical affects design outcomes?
When UX designers actively worked with AI models, they became better at discussing AI with engineers and identified important pros and cons of using AI in their designs. In an era of increasingly complex AI-driven products, bridging the communication gap between design and technical disciplines is crucial for successfu Source: Academic Publication (2023).
Where does this design research apply?
User Experience (UX) design practice involving Artificial Intelligence (AI) interfaces. It sits within user-centred design research on designdex.org.

Related research topics

technical design research · evidence on technical · does technical improve design outcomes · design studies for designers · technical and design findings · user-centred design research evidence