Ensuring Human Authorship in the Age of Generative AI: The FERC Framework for Governed Human–AI Collaboration
TL;DR
FERC (Frame–Explore–Refine–Commit), a governed interaction cycle for multi-shot human–AI collaboration that structurally safeguards human intent, judgment, and responsibility, is introduced as the cross-cutting interaction strand of the HI Manifesto series.
Abstract
Generative AI has rapidly transformed professional knowledge work, yet prevailing prompt engineering paradigms frame effective human–AI interaction primarily as a matter of crafting better prompts. Even emerging multi-round optimization approaches tend to prioritize performance, consistency, and task completion rather than authorship preservation, evaluative reframing, and accountable decision-making. This paper introduces FERC (Frame–Explore–Refine–Commit), a governed interaction cycle for multi-shot human–AI collaboration that structurally safeguards human intent, judgment, and responsibility. Drawing on creativity research, particularly models of iterative problem construction and co-evolution between generation and evaluation, FERC operationalizes metacognitive control in hybrid intelligence systems. We demonstrate how FERC reframes the Double Diamond design model for AI collaboration, provide practical behavioral guidelines, and introduce a Human–AI Collaboration Maturity Model tested multiple cross multiple executive training and educational contexts. Finally, we present FERC-bot, a metacognitive training interface that makes collaboration structures visible and measurable. To date, FERC has been applied in more than 20 organizations and with +2,000 individuals. Because the framework renders collaboration quality observable and scoreable at the process level, we outline two pathways for its formal operationalization: process-based assessment in formal education, developed at length in the HI Educational Manifesto, and auditable documentation of human contribution in professional work, from intellectual-property provenance in AI-assisted innovation to organizational human-touch accountability. Collectively, FERC is introduced as the cross-cutting interaction strand of the HI Manifesto series. Together, these contributions reposition prompt engineering from artifact optimization toward process governance, enabling scalable and authorship-preserving hybrid intelligence in educational and organizational settings.
