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Qualitative evaluation reveals context-sensitive reasoning patterns in AI within complex organizational decisions, suggesting implications for system design.
This paper introduces Human-Context Reasoning (HCR), a qualitative framework for evaluating AI behavior in organizational decision environments characterized by ambiguity, emotional complexity, and structural constraints. The study evaluates AI responses across scenarios including layoffs, burnout, ethical conflict, and power asymmetry. Findings suggest that context-sensitive reasoning patterns emerge when AI systems are constrained to operate within lived human situations rather than abstract problem-solving structures. The work proposes Human-Context Reasoning as an early conceptual framework for situationally grounded AI systems.
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Madhusudan Rana (2026) studied this question.
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