Towards A Process Model for Co-Creating AI Experiences
- URL: http://arxiv.org/abs/2104.07595v1
- Date: Thu, 15 Apr 2021 16:53:34 GMT
- Title: Towards A Process Model for Co-Creating AI Experiences
- Authors: Hariharan Subramonyam, Colleen Seifert, Eytan Adar
- Abstract summary: Thinking of technology as a design material is appealing to designers.
As a material, AI resists this approach because its properties emerge as part of the design process itself.
We investigate the co-creation process through a design study with 10 pairs of designers and engineers.
- Score: 16.767362787750418
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Thinking of technology as a design material is appealing. It encourages
designers to explore the material's properties to understand its capabilities
and limitations, a prerequisite to generative design thinking. However, as a
material, AI resists this approach because its properties emerge as part of the
design process itself. Therefore, designers and AI engineers must collaborate
in new ways to create both the material and its application experience. We
investigate the co-creation process through a design study with 10 pairs of
designers and engineers. We find that design 'probes' with user data are a
useful tool in defining AI materials. Through data probes, designers construct
designerly representations of the envisioned AI experience (AIX) to identify
desirable AI characteristics. Data probes facilitate divergent thinking,
material testing, and design validation. Based on our findings, we propose a
process model for co-creating AIX and offer design considerations for
incorporating data probes in design tools.
Related papers
- Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - I-Design: Personalized LLM Interior Designer [57.00412237555167]
I-Design is a personalized interior designer that allows users to generate and visualize their design goals through natural language communication.
I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another.
The final design is then constructed in 3D by retrieving and integrating assets from an existing object database.
arXiv Detail & Related papers (2024-04-03T16:17:53Z) - Content-Centric Prototyping of Generative AI Applications: Emerging
Approaches and Challenges in Collaborative Software Teams [2.369736515233951]
Our work aims to understand how collaborative software teams set up and apply design guidelines and values, iteratively prototype prompts, and evaluate prompts to achieve desired outcomes.
Our findings reveal a content-centric prototyping approach in which teams begin with the content they want to generate, then identify specific attributes, constraints, and values, and explore methods to give users the ability to influence and interact with those attributes.
arXiv Detail & Related papers (2024-02-27T17:56:10Z) - Geometric Deep Learning for Computer-Aided Design: A Survey [85.79012726689511]
This survey offers a comprehensive overview of learning-based methods in computer-aided design.
It includes similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds.
It provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain.
arXiv Detail & Related papers (2024-02-27T17:11:35Z) - Exploring Challenges and Opportunities to Support Designers in Learning
to Co-create with AI-based Manufacturing Design Tools [31.685493295306387]
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"
To date, we know little about how engineering designers learn to work with AI-based design tools.
arXiv Detail & Related papers (2023-03-01T02:57:05Z) - Designerly Understanding: Information Needs for Model Transparency to
Support Design Ideation for AI-Powered User Experience [42.73738624139124]
Designers face hurdles understanding AI technologies, such as pre-trained language models, as design materials.
This limits their ability to ideate and make decisions about whether, where, and how to use AI.
Our study highlights the pivotal role that UX designers can play in Responsible AI.
arXiv Detail & Related papers (2023-02-21T02:06:24Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Investigating Positive and Negative Qualities of Human-in-the-Loop
Optimization for Designing Interaction Techniques [55.492211642128446]
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Model-based computational design algorithms assist designers by generating design examples during design.
Black box methods for assistance, on the other hand, can work with any design problem.
arXiv Detail & Related papers (2022-04-15T20:40:43Z) - Design-Driven Requirements for Computationally Co-Creative Game AI
Design Tools [6.719205507619887]
We present a participatory design study that categorizes and analyzes game AI designers' goals, expectations for such tools.
We evince deep connections between game AI design and the design of co-creative tools, and present implications for future co-creativity tool research and development.
arXiv Detail & Related papers (2021-07-29T04:14:53Z) - Question-Driven Design Process for Explainable AI User Experiences [12.883597052015109]
Designers are tasked with the challenges of how to select the most suitable XAI techniques and translate them into UX solutions.
We propose a Question-Driven Design Process that grounds the user needs, choices of XAI techniques, design, and evaluation of XAI UX all in the user questions.
We provide a mapping guide between prototypical user questions and exemplars of XAI techniques, serving as boundary objects to support collaboration between designers and AI engineers.
arXiv Detail & Related papers (2021-04-08T02:51:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.