Toward AI assistants that let designers design

Peuter, Sebastiaan De; Oulasvirta, Antti; Kaski, Samuel

doi:10.1002/aaai.12077

Cited by 10 publications

(5 citation statements)

References 55 publications

(114 reference statements)

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“…These include generative design tools, data-driven approaches, and artificial intelligence (AI) tools such as ChatGPT and Google Bard [19]. Generative design tools use algorithms to generate a large number of design options, which are based on predefined rules and constraints [20]. Data-driven approaches use user data and feedback to guide ideation and design decisions [21].…”

Section: Background Idea Generation Methodsmentioning

confidence: 99%

Measuring the Impact of ChatGPT on Fostering Concept Generation in Innovative Product Design

Filippi

2023

Electronics

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The growing demand for innovative and user-centric product design has led to a growing need for effective idea generation methods. In recent years, natural language processing (NLP) tools such as ChatGPT have emerged as a promising solution for supporting idea generation in various domains. This paper investigates a framework for studying the role of ChatGPT in facilitating the ideation process in product design. This investigation measures the impact of ChatGPT on the generation of innovative concepts compared to the use of “classic” design methods. An overview of the state-of-the-art idea generation methods in product design opens the paper. Then, the paper highlights some hypotheses about the impact of ChatGPT on innovative product design, aiming for product augmentation by adding features. The paper then describes the design experience in which ChatGPT is used as a tool for concept generation. Finally, the paper analyzes the dataset, using precise metrics to characterize the participants’ performance and compare them. This analysis allows the paper to argue about the validation/rejection of the hypotheses. The paper concludes with a discussion of the implications of the findings and some suggestions for future research. Along with the paper, the Microsoft Excel workbook used to perform the data analysis is available to the readers to perform their own data collection and analysis. The workbook UX has been carefully studied and developed to make it usable by anyone. At the same time, it should be flexible enough to manage several situations characterized by different numbers of participants, product functions to implement, and generated concepts.

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Section: Background Idea Generation Methodsmentioning

confidence: 99%

Measuring the Impact of ChatGPT on Fostering Concept Generation in Innovative Product Design

Filippi

2023

Electronics

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“…• Interactive knowledge elicitation [70], combining prior knowledge from cognitive science with learning from data [71], and generative user models [72] can support more effective interactions between user and machine.…”

Section: Directionsmentioning

confidence: 99%

AI for Science: An Emerging Agenda

Berens¹,

Cranmer²,

Lawrence³

et al. 2023

Preprint

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This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling".Today's scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time-and spatial-scales, resulting in complex interactions and emergent behaviours. Understanding these phenomena -and leveraging scientific advances to deliver innovative solutions to improve society's health, wealth, and well-being -requires new ways of analysing complex systems.The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. AI for science is a rendezvous point. It brings together expertise from AI and application domains; combines modelling knowledge with engineering know-how; and relies on collaboration across disciplines and between humans and machines. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers working together to design and deploy effective AI tools.This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery.

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“…The current study addressed several gaps in the existing literature on technology adoption. Firstly, previous research has primarily focused on examining the industry changes brought about by GenAI and the feasibility of its rational use in the art and design sectors [3], [4]. However, this study performed an empirical examination by investigating 326 participants' intention to use GenAI, expanding the literature related to GenAI.…”

Section: B Contributionsmentioning

confidence: 99%

“…Previous studies have demonstrated the beneficial impact of AI across various design stages and industries. For instance, researchers have proposed an artificial intelligence-aided design framework (AIAD) to optimize the design process and enhance designers' creative abilities [3]. Additionally, [4] has shown that AI can assist application designers with their design work.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing the Adoption of Generative AI for Art Designing Among Chinese Generation Z: A Structural Equation Modeling Approach

Wang,

Zhang

2023

IEEE Access

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The integration of generative artificial intelligence (GenAI) technology in the realm of art and design has demonstrated significant positive effects on designers and related industries. The current study aimed to explore and evaluate the factors and personal traits that drive Generation Z to embrace GenAI-assisted design. The study model incorporated factors derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology Readiness Index, and the concept of trait curiosity. Empirical validation was conducted using data collected from 326 participants in the southeast of Chinese Mainland. The results of structural equation modeling indicated that: (1) Factors such as effort expectancy, price value, and hedonic motivation from UTAUT2 have a positive influence on the intention to use GenAI, while performance expectancy does not show a statistically significant effect. (2) Both optimism and creativity significantly contribute to performance expectancy, effort expectancy, price value, and hedonic motivation. (3) Trait curiosity has a significant positive impact on both optimism and the intention to use GenAI. The research findings suggest the need for further improvements in the construction and operational strategies of GenAI platforms and provide practical insights for enhancing Generation Z's intention to utilize such platforms.

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Toward AI assistants that let designers design

Cited by 10 publications

References 55 publications

Measuring the Impact of ChatGPT on Fostering Concept Generation in Innovative Product Design

Measuring the Impact of ChatGPT on Fostering Concept Generation in Innovative Product Design

AI for Science: An Emerging Agenda

Factors Influencing the Adoption of Generative AI for Art Designing Among Chinese Generation Z: A Structural Equation Modeling Approach

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