SCVHS: A Specification-Driven Methodology for AI-Assisted Professional Work

The adoption of AI tools capable of generating professional-grade artifacts—including software code, video content, written copy, visual design outputs, and data analyses—has created a structural challenge across disciplines: practitioners who delegate construction to AI without a structured re-engagement process often exhibit lower comprehension, retention, and ownership of the resulting artifacts. This challenge is domain-independent and applies wherever a human practitioner is expected to remain accountable for AI-generated work. This paper introduces SCVHS (Specify, Construct, Validate, Harden, Ship), a specification-driven methodology for AI-assisted professional work. SCVHS defines a five-phase workflow, three operating modes, and two core artifacts—the Comprehension Primitive and the Decision Log—to ensure that practitioners maintain understanding, validation capability, and ownership of AI-constructed artifacts. Grounded in cognitive load theory, scaffolding theory, mastery learning, and the generation effect, SCVHS provides a structured approach to balancing AI-assisted productivity with human comprehension. Although initially developed in a software engineering education context, the methodology is designed to generalize to any domain where AI-generated outputs require human accountability, validation, and continuous improvement. The paper presents the formal specification of SCVHS, its theoretical foundations, illustrative implementation examples, and a comparative analysis against the AWS AI-Driven Life Cycle (AI-DLC) framework.