Interpretive orchestration: An essay exploring the epistemic intersection of human intuition and machine intelligence
提出“诠释性编排”框架，将研究者从分析师转变为人类与AI协作的编排者，解决隐性知识翻译和AI生成模式判断两大挑战，适用于需要整合AI的质性研究者。
As artificial intelligence capabilities expand beyond pattern recognition to theoretical insight generation, interpretive qualitative research confronts a question of epistemic responsibility: how can scholars integrate artificial intelligence capabilities while remaining accountable for their theoretical interpretations? This essay proposes “interpretive orchestration” as a framework that transforms researchers from analysts into skilled orchestrators of human–artificial intelligence collaboration. The framework addresses two challenges that become opportunities. The translation challenge of articulating tacit knowledge (theoretical orientations, contextual understanding, and embodied intuition) into forms that artificial intelligence can process deepens researchers’ awareness of their own expertise. The judgment challenge of evaluating artificial intelligence-generated patterns for theoretical significance highlights the accountability our scholarly communities require, particularly through “1.5-order data”: patterns invisible to human perception yet requiring human interpretation for recognized theoretical significance. Three strategic models guide this orchestration: Socratic tension surfaces implicit assumptions through deliberate contradiction; Euclidean documentation enables reproducible analysis through systematic context-building; Vitruvian mastery reads across independent analytical passes for synthetic insight. By embracing orchestration, researchers discover that artificial intelligence can amplify rather than replace human capability. The future of interpretive research lies neither in rejecting artificial intelligence nor surrendering to automation but in systematic approaches to human–artificial intelligence collaboration that preserve the scholarly accountable judgment our communities require while drawing on artificial intelligence’s capacity to generate theoretical insights across scales humans cannot process alone.