AI for Computational Design Science: A Responsible Human-AI Framework and Case Study on Short-Form Video Safety Surveillance

Summary

IS scholars (Fang, Ram, Xie, Chai) propose AI4CDS, a five-phase methodological framework for computational design science in which AI actively participates in problem formulation, resource construction, design search, evaluation and knowledge abstraction while the researcher retains domain grounding, admissibility, verification and scientific judgment. Collaboration is governed by graduated trust, reversibility, auditability and differentiated reproducibility, and the framework is instantiated in a full artifact (ChildRiskGuard, F1 0.769) with documented AI interactions, rejected alternatives and audit trails. This is the IS discipline writing its own methodological rules for agentic AI in design research, and reads as a direct entry to the ISR generative-AI methods special issue.

Contribution

Queued — not yet read.

Method

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Relevance to RISE

Surfaced by the 2026-09-07 literature sweep as a high-priority candidate for the RISE knowledge base (angle: IS / management discourse on GenAI research). Verified against: Opened https://arxiv.org/abs/2609.05270 and read the full abstract, 6 authors, 2026-09-04, cs.AI. Dedupe: no match for 'design science', 'computational design', 'ai4cds', 'childrisk'.

Critique / open questions

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Key quotes

Artificial intelligence (AI) is transforming not only what information systems researchers design, but also how design research is conducted. (abstract)

Yet existing literature offers limited guidance for computational design science (CDS) when AI actively participates in problem formulation, resource construction, design search, evaluation, and knowledge abstraction. (abstract)