Somagraphic Learning™ Framework (SLF) | UAE Human-Readiness One-Pager

No current standard defines what human cognition must do before AI output exists. Somagraphic Learning™ fills this gap through pre-AI embodied sense-making: a structured, timed visual orientation layer that externalizes the learner's own conceptual state before any AI interaction begins. The three-stage sequence, Attempt → Map → Refine, positions AI exclusively at the refinement stage. The deployable artifact is the Map Before Machine™ card. It operationalizes pre-AI embodied sense-making in under 10 minutes, with no software, no IT approval, and no curriculum redesign. Deployable pen-and-paper, LMS-ready across Canvas and Moodle, and integrable as a UX onboarding layer into any AI platform before the prompt interface opens. The proposed learner competency this framework advances is Somatic AI Literacy™: the capacity to establish embodied conceptual orientation before AI interaction begins. Absent from all UNESCO, ISTE, CSTA, and US Department of Labor AI literacy frameworks. Shape-Emotion Grammar™ is the visual-cognitive scoring vocabulary of the Map stage, through which learners externalize directional relationships between concepts before AI refines them. This document is a supplementary artifact of the Somagraphic Learning™ Framework preprint (Toprani, 2026, OSF: https://doi.org/10.35542/osf.io/fnk7z_v4) IRB-ready Pilot Protocol (OSF: https://doi.org/10.17605/OSF.IO/BJWKG). Four active USPTO trademark filings: Somagraphic Learning™, Map Before Machine™, Somatic AI Literacy™, Shape-Emotion Grammar™. All framework IP and copyright remain exclusively with Devika Toprani. Licensed CC BY-NC-ND 4.0. Any integration into software platforms, AI onboarding UIs, or enterprise training curricula requires a commercial license.