Humans and AI as partners in collaborative decision-making

Our ‘human-centered’ approach is becoming increasingly essential as AI shifts from being a simple tool to a collaborative partner in making decisions. Through a research initiative focused on cognitive ergonomics, we inform AI design guidelines by exploring the cognitive demands and opportunities inherent in human-AI collaboration. By prioritizing how humans process information and make decisions, we ensure that AI tools serve as cognitive amplifiers that complement human intuition rather than overwhelming it with information. This highlights a shift from building powerful algorithms to designing intuitive interfaces and workflows that respect cognitive bandwidth. To advance these visions, we develop and test automation and augmentation strategies to balance the supply and demand of limited cognitive resources, thereby enhancing collaborative decision-making.

Rise of human-AI teams across diverse decision-making contexts

We emphasize AI’s complementary role by focusing on human interaction with its output rather than the substitution of labor. In diverse decision-making contexts, humans and AI are increasingly expected to ‘team up’ to achieve performance superior to either operating in isolation. However, such synergy—complementary team performance that neither can attain individually—is not guaranteed. Investigating the collaborative processes and influencing factors behind these outcomes remains both challenging and critical. This necessitates a research approach that captures not only retrospective and prospective insights but also moment-by-moment data on the collaborative decision-making process. Such data enables us to investigate real-time interaction patterns within and between human-AI teams and assess cognitive load at critical junctures. To better understand these underlying mechanisms, we utilize a mixed methods approach to develop and test interventions designed to enhance and maximize human-AI complementarity across various decision-making scenarios.

Bridging the expertise gap: New normal for collaborative decision-making

We propose a ‘new normal’ for collaborative decision-making that leverages human-AI complementarity to bridge critical expertise gaps. By conceptualizing the human-AI dyad as the primary collaborative unit, we redefine coordination as a network of augmented teams. Our approach synchronizes internal workflows with external multi-team systems, ensuring fluid information processing across disparate domains and experience levels. This framework transcends traditional divides in disciplines and seniority, specifically facilitating the vital bridge between experts and non-experts in increasingly interconnected work environments. We address three critical expertise divides:

Inter-disciplinary divides: In high-stakes disaster response, crisis management teams—comprising specialists from firefighting, law enforcement, and emergency medical services—must process scattered, incomplete, and inaccurate information to generate rapid, effective action plans. 

Intra-disciplinary divides (seniority gaps): In nursing, the subtle cues of sepsis diagnosis often present a significant cognitive challenge for students (novices), whereas experienced nurses recognize these patterns with greater intuitive readiness.

Expert-to-non-expert divides (transitional gaps): In community pharmacies, older adults (non-experts) frequently encounter cognitive overload when navigating over-the-counter medications. They must select safe products while accounting for age, health conditions, and potential drug interactions—often without direct guidance from pharmacists (experts). In these settings, pharmacy technicians, although they cannot offer clinical advice, provide essential navigational scaffolding, supporting the search process and bridging the gap between consumer needs and professional expertise.

To establish evidence-based guidelines for designing, training, and evaluating these augmented teams, we capture high-resolution data on collaborative decision-making processes. Our methodology integrates real-time, moment-by-moment interactions with retrospective and prospective insights, leveraging high-fidelity scenarios and emerging technologies like eye-tracking. This comprehensive approach enables us to investigate interaction patterns and map cognitive load dynamics across behavioral, physiological, and neurological dimensions. Our research suggests a significant paradigm shift: a new normal where collaborative decision-making is no longer limited by human expertise gaps but empowered by the bridge of human-AI synergy.

Moon, J., Sasangohar, F., Peres, S.C., & Son, C. (2024). Naturalistic observations of multiteam interaction networks: Implications for cognition in crisis management teams. Ergonomics, 67(3), 305-326. doi: 10.1080/00140139.2023.2221418

Son, C., Sasangohar, F., Peres, S.C., & Moon, J. (2023). Analyzing work-as-imagined and work-as-done of incident management teams using interaction episode analysis. Theoretical Issues in Ergonomics Science, 24(6), 729-757. doi: 10.1080/1463922X.2022.2153495

Moon, J., Sasangohar, F., Son, C., & Peres, S.C. (2020). Cognition in crisis management teams: An integrative analysis of definitions.Ergonomics, 63(10), 1240-1256. doi: 10.1080/00140139.2020.1781936

Son, C., Sasangohar, F., Neville, T.J., Peres, S.C., & Moon, J. (2020). Investigating resilience in emergency management: An integrative review of literature. Applied Ergonomics, 87, 103114. doi: 10.1016/j.apergo.2020.103114

Son, C., Sasangohar, F., Peres, S.C., & Moon, J. (2020). Muddling through troubled water: Resilient performance of incident management teams during Hurricane Harvey. Ergonomics, 63(6), 643-659. doi: 10.1080/00140139.2020.1752820

Son, C., Sasangohar, F., Peres, S.C., Neville, T.J., & Moon, J. (2020). Evaluation of work-as-done in information management of multidisciplinary incident management teams via interaction episode analysis. Applied Ergonomics, 84, 103031. doi: 10.1016/j.apergo.2019.103031

Son, C., Sasangohar, F., Peres, S.C., Neville, T.J., Moon, J., & Mannan, M.S. (2018). Modeling an incident management team as a joint cognitive system. Journal of Loss Prevention in the Process Industries, 56, 231-241. doi: 10.1016/j.jlp.2018.07.021

Moon, J., Lee, D., Lee, T., Ahn, J., Shin, J., Yoon, K., & Choi, D. (2015). Group decision procedure to model the dependency structure of complex systems: Framework and case study for critical infrastructures. Systems Engineering, 18(4), 323-338. doi: 10.1002/sys.21306

Gilson, A.M., Chladek, J.S., Stone, J.A., Watterson, T.L., Lehnbom, E.C., Hoffins, E.L., Berbakov, M.E., Moon, J., Jacobson, N.A., Holden, R., Gangnon, R., Pigarelli, D.L.W., Welch, L.L., Portillo, E.C., Shiyanbola, O., Gallhardt, J.D., Walker, K.D., & Chui, M.A. (2025). Older adult misuse of over-the-counter medications: Effectiveness of a novel pharmacy-based intervention to improve patient safety. Journal of Patient Safety, 21(1), 38-47. doi: 10.1097/PTS.0000000000001288

Smith, A. S., Wang, X., Moon, J., Rao, A. H., Rodriguez-Paras, C., & Sasangohar, F. (2025). Understanding preferences for a PTSD support technology among veterans: A qualitative analysis. Journal of Veterans Studies, 11(2), pp. 156–169. doi: https://doi.org/10.21061/jvs.v11i2.730

Chladek, J.S., Gilson, A.M., Stone, J.A., Berbakov, M.E., Watterson, T.L., Lehnbom, E.C., Hoffins, E.L., Hemesath, K.A., Moon, J., Welch, L.L., Pigarelli, D.L.W., Portillo, E.C., Resendiz, S.M., Mai, S., & Chui, M.A. (2024). The high prevalence and complexity of over-the-counter medication misuse in older adults. Innovation in Aging, 8(10). doi: 10.1093/geroni/igae083

Masud, F.N., Sasangohar, F., Ratnani I., Fatima, S., Hernandez, M.A., Riley, T., Fischer, J., Dhala, A., Gooch, M.E., Keeling-Johnson, K., Moon, J., & Vincent, J.L. (2024). Past, present, and future of sustainable intensive care: Narrative review and a large hospital system experience. Critical Care, 28(154). doi: 10.1186/s13054-024-04937-9

Bove, M.E., Hoffins, E.L., Stone, J.A., Gilson, A.M., Chladek, J.S., Watterson, T.L., Lehnbom, E.C., Moon, J., Holden, R., Jacobson, N.A., Shiyanbola, O., Welch, L.L., Walker, K.D., Gollhardt, J.D., & Chui, M.A. (2024). Adapting a community pharmacy intervention to improve medication safety. Journal of the American Pharmacists Association (JAPhA), 64(1), 159-168. doi: 10.1016/j.japh.2023.11.009

Lehnbom, E.C., Berbakov, M.E., Hoffins, E.L., Moon, J., Welch, L., & Chui, M.A. (2023). Elevating safe use of over-the-counter medications in older adults: A narrative review of pharmacy involved interventions and recommendations for improvement. Drugs & Aging, 40(7), 621-632. doi: 10.1007/s40266-023-01041-5

Markert, C., Moon, J., & Sasangohar, F. (2021). Smart telehealth systems for the aging population (pp. 101-117). In Moallem. A. (Ed.) Smart and intelligent systems: The human elements in artificial intelligence, robotics, and cybersecurity (1st ed.). CRC Press. eBook ISBN: 9781003215349. doi: 10.1201/9781003215349

Bae, S.Y., Moon, J., & Morrison, J.R. (2017). Design of engineering courses as a service: Emotions, senses, and implementation. International Journal of Engineering Education, 33(5), 1561-1574. ISSN: 0949-149X