Volume 8: 32nd International Conference on Design Theory and Methodology (DTM) 2020
DOI: 10.1115/detc2020-22555
|View full text |Cite
|
Sign up to set email alerts
|

Analyzing the Characteristics of Cognitive-Assistant-Facilitated Ideation Groups

Abstract: The rapid digitalization of the world has affected engineering and design in a variety of ways, including the introduction of new computer-aided ideation tools. Cognitive assistants (CA), an increasingly common digital technology, use natural-language processing and artificial intelligence to provide computational support. Because cognitive assistants are capable of emulating humans in some tasks, they may be suited to support brainstorming activities when trained coaches or facilitators are not available. Thi… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
6
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
7

Relationship

3
4

Authors

Journals

citations
Cited by 10 publications
(6 citation statements)
references
References 0 publications
0
6
0
Order By: Relevance
“…In the conceptual design stage, researchers often used NLP (8 out of 28 papers) and deep learning (11 out of 28 papers) to translate design requirements to specifications, ideate designs and generate alternative design concepts for further analysis. In addition to these methods, we observed: three agent-based modeling methods applied to facilitate ideation (Maier et al 2020), to transfer design strategies from human to computer , and to generate concepts from standard grammatic representations and abstract designer heuristics (Puentes et al 2018); three machine learning methods for recommending design features (Yao et al 2017), evaluating design features (Vasantha et al 2022), and…”
Section: Ai In Conceptual Designmentioning
confidence: 99%
“…In the conceptual design stage, researchers often used NLP (8 out of 28 papers) and deep learning (11 out of 28 papers) to translate design requirements to specifications, ideate designs and generate alternative design concepts for further analysis. In addition to these methods, we observed: three agent-based modeling methods applied to facilitate ideation (Maier et al 2020), to transfer design strategies from human to computer , and to generate concepts from standard grammatic representations and abstract designer heuristics (Puentes et al 2018); three machine learning methods for recommending design features (Yao et al 2017), evaluating design features (Vasantha et al 2022), and…”
Section: Ai In Conceptual Designmentioning
confidence: 99%
“…Mis-diagnosed errors, or errors or actions taken by an automated aid where the cause is incorrectly perceived by the operator, made by automation have been found to significantly impact user error and bias (Sauer et al, 2016 ). Additionally, previous work by Maier et al ( 2020 ) found that a lack of transparency can lead users to incorrectly diagnose built-in functions as errors, leading to frustration. A lack of transparency has also been empirically linked to the slow adoption of robo-advisors (Zhang et al, 2021 ).…”
Section: Related Workmentioning
confidence: 99%
“…In addition, AI assistance has been used at the concept generation (Camburn, Arlitt, et al, 2020), concept evaluation (Camburn, He, et al, 2020), prototyping (Dering et al, 2018), and manufacturing (Williams et al, 2019) stages,. Work has studied the impacts of AI assistance in aspects of engineering design, including decisionmaking, optimization, and computational tasks Rao et al, 1999), and its effects on mental workload, effort, and frustration (Maier et al, 2020(Maier et al, , 2021. Bang et al (2018) introduced DAPHNE as an intelligent cognitive assistant developed for providing support in system architecting, specifically for designing a constellation of satellites for Earth observation.…”
Section: Introuctionmentioning
confidence: 99%