Hostname: page-component-54dcc4c588-64p75 Total loading time: 0 Render date: 2025-10-07T15:54:59.935Z Has data issue: false hasContentIssue false
  • English
  • Français

Are Confident Designers Good Teammates to Artificial Intelligence?: A Study of Self-Confidence, Competence, and Collaborative Performance

Published online by Cambridge University Press:  26 May 2022

L. Chong ,
K. Kotovsky  and
J. Cagan
L. Chong
Affiliation:
Carnegie Mellon University, United States of America
K. Kotovsky
Affiliation:
Carnegie Mellon University, United States of America
J. Cagan*
Affiliation:
Carnegie Mellon University, United States of America
*
cagan@cmu.edu

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

For successful human-artificial intelligence (AI) collaboration in design, human designers must properly use AI input. Some factors affecting that use are designers’ self-confidence and competence and those variables' impact on reliance on AI. This work studies how designers’ self-confidence before and during teamwork and overall competence are associated with their performance as teammates, measured by AI reliance and overall team score. Results show that designers’ self-confidence and competence have very different impacts on their collaborative performance depending on the accuracy of AI.

Keywords

artificial intelligence (AI)teamworkcollaborative designdecision makingdesign cognition

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1531 - 1540
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2022.

References

Camburn, B., He, Y., Raviselvam, S., Luo, J. and Wood, K. (2020), “Machine learning-based design concept evaluation”, Journal of Mechanical Design, Vol. 142 No. 3, pp. 031113. 10.1115/1.4045126Google Scholar
Chen, H.Q., Honda, T. and Yang, M.C. (2013), “Approaches for identifying consumer preferences for the design of technology products: A case study of residential solar panels”, Journal of Mechanical Design, Vol. 135 No. 6, pp. 061007. 10.1115/1.4024232CrossRefGoogle Scholar
Chong, L., Raina, A., Goucher-Lambert, K., Kotovsky, K. and Cagan, J. (2022), “Collaborative design decision-making with artificial intelligence: Exploring the evolution and impact of human confidence in AI and in themselves”, Submitted.Google Scholar
Fischhoff, B., Slovic, P. and Lichtenstein, S. (1977), “Knowing with certainty: The appropriateness of extreme confidence”, Journal of Experimental Psychology: Human Perception and Performance, Vol. 3 No. 4, pp. 552–564. 10.1037/0096-1523.3.4.552Google Scholar
Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., et al. . (2022), “Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design”, Journal of Mechanical Design, Vol. 144 No. 2, pp. 021405. 10.1115/1.4052488CrossRefGoogle Scholar
Harter, S. (1999), The Construction of the Self: A Developmental Perspective, Guilford Press, New York.Google Scholar
James Wilson, H. and Daugherty, P.R. (2018), Collaborative intelligence: Humans and AI are joining forces. [online] Harvard Business Review. Available at: https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces (accessed 20.10.2021).Google Scholar
Kvam, P.D., Pleskac, T.J., Yu, S. and Busemeyer, J.R. (2015), “Interference effects of choice on confidence: Quantum characteristics of evidence accumulation”, Proceedings of the National Academy of Sciences, National Academy of Sciences, Vol. 112 No. 34, pp. 10645–10650. 10.1073/pnas.1500688112CrossRefGoogle ScholarPubMed
Larwood, L. and Whittaker, W. (1977), “Managerial myopia: Self-serving biases in organizational planning”, Journal of Applied Psychology, Vol. 62 No. 2, pp. 194–198. 10.1037/0021-9010.62.2.194Google Scholar
Lee, J.D. and Moray, N. (1994), “Trust, self-confidence, and operators’ adaptation to automation”, International Journal of Human-Computer Studies, Vol. 40 No. 1, pp. 153–184. 10.1006/ijhc.1994.1007Google Scholar
Lee, J.D. and See, K.A. (2004), “Trust in automation: Designing for appropriate reliance”, Human Factors, Vol. 46 No. 1, pp. 50–80. 10.1518/hfes.46.1.50_30392CrossRefGoogle ScholarPubMed
Malmendier, U. and Tate, G. (2005), “CEO Overconfidence and Corporate Investment”, The Journal of Finance, Vol. 60 No. 6, pp. 2661–2700. 10.1111/j.1540-6261.2005.00813.xCrossRefGoogle Scholar
Myers, D.G. and Twenge, J.M. (2018), Social psychology (13th Edition), McGraw-Hill Education, New York.Google Scholar
Song, B., Zurita, N.F.S., Zhang, G., Stump, G., Balon, C., et al. . (2020), “Toward hybrid teams: A platform to understand human-computer collaboration during the design of complex engineered systems”, Proceedings of the Design Society: DESIGN Conference, Cambridge University Press, Virtual, Vol. 1, pp. 1551–1560. 10.1017/dsd.2020.68Google Scholar
Svenson, O. (1981), “Are we all less risky and more skillful than our fellow drivers?”, Acta Psychologica, Vol. 47 No. 2, pp. 143–148. 10.1016/0001-6918(81)90005-6CrossRefGoogle Scholar
Vialle, I., Santos-Pinto, L. and Rulliere, J.-L. (2011), “Self-Confidence and Teamwork: An Experimental Test”, SSRN Electronic Journal, Gate Working Paper No. 1126. 10.2139/SSRN.1943453Google Scholar
Williams, G., Meisel, N.A., Simpson, T.W. and McComb, C. (2019), “Design repository effectiveness for 3D convolutional neural networks: Application to additive manufacturing”, Journal of Mechanical Design, Vol. 141 No. 11, pp. 111701. 10.1115/1.4044199/955346CrossRefGoogle Scholar
Zhang, G., Raina, A., Cagan, J. and McComb, C. (2021), “A cautionary tale about the impact of AI on human design teams”, Design Studies, Vol. 72, pp. 100990. 10.1016/j.destud.2021.100990Google Scholar