Interpretive orchestration: an essay exploring the epistemic intersection of human intuition and machine intelligence
Author(s)
Lin, Xule
Corley, Kevin
Type
Journal Article
Abstract
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.
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.
Date Issued
2026-04-27
Date Acceptance
2026-04-22
Citation
Strategic Organization, 2026
ISSN
1476-1270
Publisher
SAGE Publications
Journal / Book Title
Strategic Organization
Copyright Statement
© The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
License URL
Identifier
10.1177/14761270261448645
Subjects
epistemic responsibility
generative AI
human-AI collaboration
interpretive orchestration
qualitative methods
tacit knowledge
Publication Status
Published online
Date Publish Online
2026-04-27