MARLA: A Conceptual Scaffold for Regulatory Learning under the EU AI Act

The paper introduces MARLA, a conceptual scaffold for regulatory learning under the EU AI Act, outlining a five‑stage cycle—Map, Assess, Report, Learn, Adapt—focused on implementing legal requirements into socio‑technical practices across local, national, and European levels. It emphasizes that regulatory learning must translate evidence from implementation into governance and legal knowledge to support consistent interpretation, effective oversight, and adaptation as technologies evolve. The scaffold is deliberately non‑prescriptive, offering a shared vocabulary for technical and legal stakeholders, and is illustrated through two pilot case studies and a prospective national‑to‑European illustration.

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