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Design science research (DSR) increasingly depends on engaging with large bodies of artifact-oriented research beyond the ‘traditional’ DSR literature. Yet, extracting and formalizing design-relevant insights from non-DSR publications remains highly manual because prescriptive intent is typically embedded in technical descriptions, evaluation results, and trade-off discussions rather than expressed as reusable design knowledge. By following an exploring-by-building design logic, we present AIDE. AIDE is a human-in-the-loop, AI-assisted pipeline that excavates latent design knowledge from non-DSR corpora and transforms it into formalized representations. We instantiate AIDE and demonstrate its feasibility in an interdisciplinary corpus on AI-enabled circular economy, reconstructing one structured design principle per study under expert validation. The paper contributes (1) a method for design knowledge excavation, (2) an instantiation that operationalizes transparent human–AI collaboration, and (3) a broadened perspective on DSR as an integrating meta-discipline that can systematically draw on latent knowledge across disciplinary boundaries at scale.

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Making the Implicit Explicit: A Human-In-The-Loop AI Pipeline for Excavating and Making Use of Latent Design Knowledge

  • 会议论文

  • 发表日期：

    2026年

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  • Timo Strohmann,
  • Linda Sagnier Eckert,
  • Daniel Heinz,
  • Thorsten Schoormann,
  • Christoph Hoppe-Ludwig,
  • Anne Ixmeier

摘要

Design science research (DSR) increasingly depends on engaging with large bodies of artifact-oriented research beyond the ‘traditional’ DSR literature. Yet, extracting and formalizing design-relevant insights from non-DSR publications remains highly manual because prescriptive intent is typically embedded in technical descriptions, evaluation results, and trade-off discussions rather than expressed as reusable design knowledge. By following an exploring-by-building design logic, we present AIDE. AIDE is a human-in-the-loop, AI-assisted pipeline that excavates latent design knowledge from non-DSR corpora and transforms it into formalized representations. We instantiate AIDE and demonstrate its feasibility in an interdisciplinary corpus on AI-enabled circular economy, reconstructing one structured design principle per study under expert validation. The paper contributes (1) a method for design knowledge excavation, (2) an instantiation that operationalizes transparent human–AI collaboration, and (3) a broadened perspective on DSR as an integrating meta-discipline that can systematically draw on latent knowledge across disciplinary boundaries at scale.