Collaborative artificial intelligence (AI) for idea generation in design teams
Abstract/Contents
- Abstract
- Engineering design teams are critical for innovation and in solving the increasingly complex problems we encounter. As Artificial Intelligence (AI) systems are providing capabilities that enable humans to perform well beyond what they might have been able to, an understanding of how engineering design teams can leverage AI systems is critical. We have seen these AI capabilities for example in human-AI art generation; software development where computers write a significant portion of code; and even in radiology where AI identifies patterns that humans cannot. This work takes the position that collaborative human-AI teams are the next frontier, and explores this in two studies. The first one involved a voice/text AI collaborator by developing, then testing various vocabularies. It concluded by highlighting gaps in the current models and datasets, which need to be resolved in order to implement such voice/text AI collaborators. The second involved a sketching AI, and used an open source app. Though it worked well as a divergent ideation collaborator, we identified gaps in the variety of suggestions that the AI presented. In addition to this, it demonstrated that when given a prompt to design for, human-AI teams generated more ideas on average, as well as more that were rated higher by two trained research assistants. By demonstrating that Human-AI teams can outperform Human-Human teams on a design task, we provide empirical evidence of AI's potential in design teams. As we enter to the next phase of human-machine collaboration, this work contributes towards a conceptual design of a new class of AI systems that observe humans and come up with suggestions in real time, enabling researchers to design and test new concepts. Others can apply the methods we have demonstrated to explore different environmental contexts such as distributed teams or mixed reality; as well as other modes of interactions during collaboration such as gestures and non-verbal cues.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Makokha, Joseph Maloba |
|---|---|
| Degree supervisor | Leifer, Larry J |
| Thesis advisor | Leifer, Larry J |
| Thesis advisor | Cockayne, William R |
| Thesis advisor | Landay, James A, 1967- |
| Degree committee member | Cockayne, William R |
| Degree committee member | Landay, James A, 1967- |
| Associated with | Stanford University, Department of Mechanical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Joseph Maloba Makokha. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/sn290cc2516 |
Access conditions
- Copyright
- © 2022 by Joseph Maloba Makokha
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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