Scenario Based Government Training: Operationalizing Epistemic Accountability through Auditable Institutional Reasoning (The ELENA Framework)

This paper proposes a conceptual framework for scenario based government employee training that uses AI to support, not replace, the judgment of public servants. The framework is organized around four core principles: scenario based learning grounded in case based reasoning, adaptive learning tied to measured competence, hierarchical knowledge organization tied to legal authority, and source based accountability tied to validated source items. A fifth design commitment, linguistic parity across the working languages of the jurisdiction, is treated as architectural rather than downstream. The framework, named ELENA (Epistemic Learning Environment with Notated Accountability; Spanish: Entorno de Lecciones Epistémicas con Notación Auditable), centers on the Concept Domain Claim (CDC) trace, a six-field per-output accountability artifact that documents the substantive claim, layer of authority, source provision, version timestamp, confidence indicator, and adaptation context for every AI-generated training output. The CDC trace makes the reasoning behind each output inspectable, auditable, and reviewable by supervisors, learners, and institutional stakeholders. The framework specifies scenario design rules for epistemic accountability, a competency progression from procedural cases to institutional judgment, an assessment mechanism that measures judgment quality rather than recall, engineered friction against automation bias, and a trunk-and-branch validation protocol that respects the capacity constraints of real public sector institutions. A randomized controlled trial is proposed as the primary evaluation design, with stepped-wedge and quasi-experimental alternatives for settings where randomization is not feasible. The framework integrates recent work on legal ontology (De Martim 2025; Hoekstra et al. 2007), W3C PROV-O provenance standards (Lebo et al. 2013), and Akoma Ntoso statute representation (OASIS 2018), as well as neuro-symbolic accountability systems for public benefits (Sunny 2025) and empirical evidence on authentic assessment integrity in the presence of generative AI (Kofinas et al. 2025). The framework is integrated for the Commonwealth of Puerto Rico, including its layered legal hierarchy of federal, Commonwealth, and municipal authority, its bilingual Spanish and English operating conditions, and the seventy-eight municipalities operating under the Municipal Code (Law 107-2020) and the human resources framework of Law 8-2017. v2.0, June 2026. Originally published May 2026.