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Is It Human or Is It Artificial Intelligence? Discerning the Impact and Effectiveness of Process Managers Based on the Manager's Identity

Published online by Cambridge University Press:  26 May 2022

J. T. Gyory ,
K. Kotovsky  and
J. Cagan
J. T. Gyory*
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
*
jgyory@andrew.cmu.edu

Abstract

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This work studies the perception of the impacts of AI and human process managers during a complex design task. Although performance and perceptions by teams that are AI- versus human-managed are similar, we show that how team members discern the identity of their process manager (human/AI), impacts their perceptions. They discern the interventions as significantly more helpful and manager sensitive to the needs of the team, if they believe to be managed by a human. Further results provide deeper insights into automating real-time process management and the efficacy of AI to fill that role.

Keywords

artificial intelligence (AI)design managementdesign teamsproblem solving

Information

Type
Article
Information
Proceedings of the Design Society , Volume 2: DESIGN2022 , May 2022 , pp. 1579 - 1588
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, 142(3), p.031113. doi: 10.1115/1.4045126.Google Scholar
Costa, A., Novais, P. and Julian, V., 2018. A survey of cognitive assistants. In Personal Assistants: Emerging Computational Technologies (pp. 3–16). Springer, Cham. doi:10.1007/978-3-319-62530-0_1.CrossRefGoogle Scholar
Dellermann, D., Ebel, P., Söllner, M. and Leimeister, J.M., 2019. “Hybrid intelligence.” Bus Inf Syst Eng. doi:10.1007/s12599-019-00595-2.CrossRefGoogle Scholar
de Visser, E.J., Krueger, F., McKnight, P., Scheid, S., Smith, M., Chalk, S. and Parasuraman, R., 2012, September. The world is not enough: Trust in cognitive agents. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 56, No. 1, pp. 263–267). Sage CA: Los Angeles, CA: Sage Publications. doi: 10.1177/1071181312561062.Google Scholar
Ezer, N., Bruni, S., Cai, Y., Hepenstal, S.J., Miller, C.A. and Schmorrow, D.D., 2019, November. Trust Engineering for Human-AI Teams. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 63, No. 1, pp. 322–326). Sage CA: Los Angeles, CA: SAGE Publications. doi: 10.1177/1071181319631264.CrossRefGoogle Scholar
Graesser, A.C., VanLehn, K., Rosé, C.P., Jordan, P.W. and Harter, D., 2001. Intelligent tutoring systems with conversational dialogue. AI magazine, 22(4), pp.39–39. doi: 10.1609/aimag.v22i4.1591.Google Scholar
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S. and Yang, G.Z., 2019. XAI—Explainable artificial intelligence. Science Robotics, 4(37). Doi: 10.1126/scirobotics.aay7120.CrossRefGoogle ScholarPubMed
Gyory, J.T., Cagan, J. and Kotovsky, K., 2019. Are you better off alone? Mitigating the underperformance of engineering teams during conceptual design through adaptive process management. Research in Engineering Design, 30(1), pp.85–102. doi: 10.1007/s00163-018-00303-3.CrossRefGoogle Scholar
Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K. and Cagan, J., 2022. Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design, 144(2). doi: 10.1115/1.4052488.CrossRefGoogle Scholar
Gyory, J.T., Song, B., Cagan, J. and McComb, C., 2021. Communication in AI-Assisted Teams During an Interdisciplinary Drone Design Problem. Proceedings of the Design Society, 1, pp.651–660. doi:10.1017/pds.2021.65.Google Scholar
Hoff, K.A. and Bashir, M., 2015. Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3), pp.407–434. doi: 10.1177/0018720814547570.CrossRefGoogle ScholarPubMed
Hoffman, R.R., Johnson, M., Bradshaw, J.M. and Underbrink, A., 2013. Trust in automation. IEEE Intelligent Systems, 28(1), pp.84–88. doi: 10.1109/MIS.2013.24.CrossRefGoogle Scholar
Hu, Y. and Taylor, M.E., A Computer-Aided Design Intelligent Tutoring System Teaching Strategic Flexibility. In: 2016 ASEE Annual Conference & Exposition Proceedings. ASEE Conferences. Epub ahead of print 2016. doi: 10.18260/p.27208.Google Scholar
“HyForm.” GitHub. https://github.com/hyform/drone-testbed-server/releases/tag/2021-March-v2, Accessed April 23, 2021.Google Scholar
Koch, J., 2017, March. Design implications for Designing with a Collaborative AI. In 2017 AAAI Spring Symposium Series.Google Scholar
Lake, B.M., Ullman, T.D., Tenenbaum, J.B. and Gershman, S.J., 2017. “Building machines that learn and think like people.” Behavioral and brain sciences, 40. doi:10.1017/S0140525X16001837.CrossRefGoogle ScholarPubMed
Lee, J.D. and See, K.A., 2004. Trust in automation: Designing for appropriate reliance. Human factors, 46(1), pp.50–80. doi: 10.1518/hfes.46.1.50_30392.CrossRefGoogle ScholarPubMed
Lewkowicz, J. (2020). Augmented intelligence will help, not replace, human workers. [online]Software Development Times. Available at: https://sdtimes.com/ai/augmented-intelligence-will-help-not-replace-human-workers/.Google Scholar
Paul, R., Drake, J.R. and Liang, H., 2016. Global virtual team performance: The effect of coordination effectiveness, trust, and team cohesion. IEEE Transactions on Professional Communication, 59(3), pp.186–202. doi: 10.1109/TPC.2016.2583319.Google Scholar
Roll, I., Wiese, E.S., Long, Y., Aleven, V. and Koedinger, K.R., 2014. “Tutoring self-and co-regulation with intelligent tutoring systems to help students acquire better learning skills.” Design recommendations for intelligent tutoring systems, 2, pp.169–182.Google Scholar
Sadiku, M.N. and Musa, S.M., 2021. Augmented Intelligence. In A Primer on Multiple Intelligences (pp. 191–199). Springer, Cham. doi: 10.1007/978-3-030-77584-1_15.CrossRefGoogle Scholar
Schimpf, C., Huang, X., Xie, C., Sha, Z. and Massicotte, J., 2019, June. Developing Instructional Design Agents to Support Novice and K-12 Design Education. In ASEE annual conference & exposition.Google Scholar
Song, B., Gyory, J.T., Zurita, N.F.S., Zhang, G., Stump, G., Balon, C., Miller, S.W., Yukish, M., McComb, C., and Cagan, J. 2022. “Decoding the agility of artificial intelligence-assisted human design teams.” Design Studies, 79. doi:10.1016/j.destud.2022.101094.CrossRefGoogle Scholar
Zhang, G., Zurita, N.F.S., Stump, G., Song, B., Cagan, J. and McComb, C., 2021. Data on the design and operation of drones by both individuals and teams. Data in brief, Journal of Mechanical Design, 36, p.107008. doi:10.1016/j.dib.2021.107008.Google Scholar