Hybrid Reasoning Systems That Prioritize and Enhance Human Intelligence

Abstract:In a world of accelerating change, there is a need for wise and adaptive human reasoning. Integrating AI capabilities with human guidance offers promise, though human reasoning itself is often hasty, shortsighted, and error-prone, and no clear framework exists for combining human reasoning strategies with AI across diverse tasks. This article proposes a framework for human-centered hybrid reasoning systems that engage and enhance human reasoning abilities ranging from granular data analysis to high-level reflection and wisdom. The framework was developed through a conceptual synthesis combining: (1) established strategies for enhancing human reasoning, (2) AI design approaches that favor pre-conclusive engagement over the generation of conclusions, and (3) the treatment of reasoning as a collection of distinct, individually supportable modes. This synthesis produced a distinctive framework for broad-spectrum reasoning enhancement from which a typology of reasoning modes, a system architecture, and a high-level research agenda were derived.

20 pages; 6 figures; 2 tables. This paper underwent significant extension and revision from prior archived version

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2504.13477 [cs.HC]

(or arXiv:2504.13477v3 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2504.13477 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1080/10447318.2026.2735159 Focus to learn more DOI(s) linking to related resources

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