Human And Artificial Intelligence Collaboration For Socially Shared

Bonisiwe Shabane

-Nov 29, 2025, 9:20 PM

human and artificial intelligence collaboration for socially shared

The following article was written by Dr. Cornelia C. Walther, a visiting scholar at Wharton and director of global alliance POZE. A humanitarian practitioner who spent over 20 years at the United Nations, Walther’s current research focuses on leveraging AI for social good. Imagine a neurosurgeon who faces a complex, high-risk brain surgery. Despite years of experience, the case presents unpredictable variables.

Instead of relying solely on intuition, she turns to an AI-powered surgical assistant, which analyzes millions of similar cases in seconds, predicting complications and suggesting the most precise approach. As she operates, her expertise guides the procedure while the AI continuously adjusts recommendations in real time based on the patient’s vitals. When an unexpected complication arises, the AI flags an anomaly milliseconds before human detection, allowing the surgeon to act instantly and save the patient’s life. The AI extended the human’s capabilities without replacing her judgment. This is hybrid intelligence (HI) in action — natural and artificial intelligence working together, amplifying strengths, compensating for weaknesses, and achieving what neither could alone. By understanding and harnessing HI, organizations can move beyond incremental efficiency gains to unlock strategic, sustainable outcomes that future-proof the enterprise while improving the well-being of the people involved.

In the following sections, I explain how the multidimensional set-up of natural intelligence intertwines with AI to create HI, and provide a practical framework to help organizations leverage these ideas systematically and cost-effectively. Let’s start with a quick overview of the primary forms of intelligence referenced in this article: Socially shared regulation in learning (SSRL) contributes to successful collaborative learning (CL). Empirical research into SSRL has received considerable attention recently, with increasingly available multimodal data, advanced learning analytics (LA), and artificial intelligence (AI) providing promising research avenues. Yet, integrating these with traditional datasets remains a challenge in SSRL research due to the misalignment between theoretical constructs, methodological assumptions, and data structure. To address this challenge and expand our understanding of the nature of SSRL, the present research adopted a human–AI collaboration approach in a three-layer analysis to examine group interactions in response to cognitive and...

Two-level theoretical lenses — macro-level (regulatory aspects) and micro-level (deliberative interactions) — were used to analyze 2,125 utterances from video-recorded tasks of ten groups of three Finnish secondary students (N=30). Results showed two types of deliberation patterns for SSRL, namely 1) the Plan and Implementation Approach (PIA) associated with adaptive patterns, and 2) the Trials and Failure Approach (TFA) associated with maladaptive patterns. Our findings revealed that groups often fail to recognize, or are ill-equipped to respond to, emerging regulatory needs. These findings advance SSRL theories and research methodologies by utilizing AI-enhanced LA to offer new insights into group dynamics and regulatory strategies. Abbott, A., & Tsay, A. (2000).

Sequence analysis and optimal matching methods in sociology: Review and prospect. Sociological Methods & Research, 29(1), 3–33. https://doi.org/10.1177/0049124100029001001 Andrade, A., Delandshere, G., & Danish, J. A. (2016).

Using multimodal learning analytics to model student behavior: A systematic analysis of epistemological framing. Journal of Learning Analytics, 3(2), 282–306. https://doi.org/10.18608/jla.2016.32.14 Bakhtiar, A., & Hadwin, A. (2020). Dynamic interplay between modes of regulation during motivationally challenging episodes in collaboration.

Frontline Learning Research, 8(2), 1–34. https://doi.org/10.14786/flr.v8i2.561 Bowman, D., Swiecki, Z., Cai, Z., Wang, Y., Eagan, B., Linderoth, J., & Williamson Shaffer, D. (2021). The mathematical foundations of epistemic network analysis. In A.

R. Ruis & S. B. Lee (Eds.), Advances in quantitative ethnography: Second international conference, ICQE 2020, Malibu, CA, USA, February 1–3, 2021, Proceedings (pp. 91–105). Springer Cham.

https://doi.org/10.1007/978-3-030-67788-6_7 Correspondence should be sent to Pranav Gupta, Gies College of Business, University of Illinois, Urbana‐Champaign, 6 Wohlers Hall, 1206 S. Sixth St., Champaign, IL 61820, USA. Email: pranavgu@illinois.edu Revised 2023 Jun 12; Received 2022 Jun 30; Accepted 2023 Jun 12; Issue date 2025 Apr. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human‐Machine Intelligence (COHUMAIN).

It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence... We connect this with synergistic work on a compatible cognitive architecture, instance‐based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine... Jacob Taylor, Thomas Kehler, Sandy Pentland, Martin Reeves Janice C.

Eberly, Molly Kinder, Dimitris Papanikolaou, Lawrence D. W. Schmidt, Jón Steinsson Rosanne Haggerty, Ruby Bolaria Shifrin, Jacob Taylor, Kershlin Krishna, Sara Bronin, Nick Cain, Xiomara Cisneros, Adam Ruege, Henri Hammond-Paul, Jamie Rife, Josh Humphries, Beth Noveck

Human And Artificial Intelligence Collaboration For Socially Shared

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