aceAI-human collaborative decision-making and how its dynamics and typologies shape decision outcomes
Source article: Advancing Decision-Making through AI-Human Collaboration: A Systematic Review and Conceptual Framework
Abstract The interplay between humans and artificial intelligence (AI) in decision-making has become increasingly intricate and significant. Despite rapid advancements, the literature remains fragmented, with limited integrative frameworks to explain how AI-human dynamics and decision-making typologies shape outcomes. This study addresses this critical gap by conducting a systematic review and bibliometric analysis of 627 articles, culminating in a novel conceptual framework. The framework identifies two critica…
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On 2026-04-03, a peer-reviewed article reported a systematic review and bibliometric analysis of 627 articles on human-AI decision-making. The authors identified two critical dimensions, AI-human dynamics and decision typologies, and proposed a novel conceptual framework comprising four paradigms: adaptive intuitive, programmed algorithmic, interpretive analytical, and integrative hybrid decision-making.
The framework matters because it moves beyond fragmented prior literature to clarify mechanisms and trade-offs in hybrid decision systems, offering actionable insights for organizations navigating AI-driven environments. As a conceptual synthesis rather than an empirical trial of a specific deployment, its impact remains theoretical, laying a foundation for future research on adaptive decision systems amid accelerating technological change.
- Systematic review and bibliometric analysis covered 627 articles on AI-human decision-making.
- Framework identifies two critical dimensions: AI-human dynamics and decision typologies that shape decision outcomes.
- Proposes four distinct paradigms: adaptive intuitive decision, programmed algorithmic decision, interpretive analytical decision and integrative hybrid decision.
A systematic review and bibliometric analysis of 627 articles produced a conceptual framework with two dimensions and four paradigms that advances theoretical understanding of hybrid decision-making and provides actionable insights for organizations in AI-driven environments.
Existing literature on AI-human decision-making was fragmented and lacked integrative frameworks to explain how AI-human dynamics and decision typologies shape outcomes despite increasingly intricate human-AI interplay.
The rundown
The authors conducted a systematic review and bibliometric analysis of 627 articles to address fragmentation in the field. From this corpus they distilled two critical dimensions that shape decision outcomes: AI-human dynamics and decision typologies.
They synthesized these dimensions into four distinct paradigms of AI-human collaborative decision-making: adaptive intuitive decision, programmed algorithmic decision, interpretive analytical decision and integrative hybrid decision. The work is positioned as advancing theoretical understanding of hybrid systems and elucidating mechanisms and trade-offs inherent in collaboration.
Sources
- Peer-reviewedGroup Decision and Negotiation2026-04-03
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