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

In: AI, Society and Digital Transformation

Author

Listed:
  • Hootan Kamran

    (Northeastern University)

  • Atanaz Dorrani

    (City of Mississauga)

  • Houman Kamran

    (University of the Pacific)

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.

Suggested Citation

  • Hootan Kamran & Atanaz Dorrani & Houman Kamran, 2026. "The Algorithmic Reconfiguration of Qualitative Inquiry: Navigating AI-Driven Efficiency and Interpretive Richness," Lecture Notes in Operations Research, in: Xiaolei Xie & Kejia Hu & Guiping Hu & Weiwei Chen & Robin Qiu (ed.), AI, Society and Digital Transformation, pages 250-261, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-13116-4_20
    DOI: 10.1007/978-3-032-13116-4_20
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