The Algorithmic Reconfiguration of Qualitative Inquiry: Navigating AI-Driven Efficiency and Interpretive Richness

Hootan Kamran (), Atanaz Dorrani () and Houman Kamran ()
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Hootan Kamran: Northeastern University
Atanaz Dorrani: City of Mississauga
Houman Kamran: University of the Pacific

A chapter in AI, Society and Digital Transformation, 2026, pp 250-261 from Springer

Abstract: Abstract Artificial Intelligence (AI) profoundly reshapes research methodologies, offering opportunities and challenges. This paper critically examines the algorithmic reconfiguration of qualitative inquiry, focusing on the tension between AI-driven efficiency and interpretive richness. It explores how AI capabilities in large-scale data processing, pattern recognition, multimodal analysis, and cross-lingual understanding alter qualitative research. While AI enhances speed, cost reduction, and overcomes language/survey barriers, these efficiencies risk superficiality, algorithmic bias, and ethical dilemmas. The paper discusses strategies for navigating this tension, evolving researcher skills, and the imperative for human-centricity to ensure AI genuinely augments insights, particularly within Service Science and digital transformation.

Keywords: Artificial Intelligence; Qualitative Research; Quantitative Research; Research Methodology; Algorithmic Reconfiguration; Interpretive Richness; AI Ethics; Service Science; Digital Transformation; Semantic Embeddings; AI-Driven Efficiency; Human-AI Collaboration; Open-Ended Surveys (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-13116-4_20

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DOI: 10.1007/978-3-032-13116-4_20

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