EXPRESS: Interpretive Orchestration: An Essay Exploring the Epistemic Intersection of Human Intuition and Machine Intelligence

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 AI 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-AI collaboration. The framework addresses two challenges that become opportunities. The translation challenge of articulating tacit knowledge (theoretical orientations, contextual understanding, embodied intuition) into forms AI can process deepens researchers’ awareness of their own expertise. The judgment challenge of evaluating AI-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 AI can amplify rather than replace human capability. The future of interpretive research lies neither in rejecting AI nor surrendering to automation, but in systematic approaches to human-AI collaboration that preserve the scholarly-accountable judgment our communities require while drawing on AI’s capacity to generate theoretical insights across scales humans cannot process alone.