TruthGrid-AI: A Cyber-Physical Verification Framework for Multimodal Generative AI Literacy in Resource-Constrained Environments
Year: 2026
Type: preprint
Abstract: The exponential proliferation of multimodal generative artificial intelligence (GenAI)—encompassing latent diffusion imagery, real-time voice cloning, synthetic news anchors, and automated hallucination pipelines—has precipitated an epistemic crisis among digital youth. Traditional media literacy paradigms relying on passive checklists (e.g., the CRAAP test) and didactic lectures are fundamentally obsolete against sub-second algorithmic synthesis that produces syntactically pristine text and photorealistic imagery. To address this pedagogical gap, this paper introduces TruthGrid-AI, an open-source, hybrid cyber-physical verification framework designed to cultivate cognitive resilience and forensic verification reflexes. TruthGrid-AI operationalizes psychological inoculation theory (prebunking) through a tangible tabletop interaction paradigm: students collaborate using low-cost, printable physical cards paired with an asynchronous mobile application and ultra-low-latency edge inference via Groq Language Processing Units (LPUs) executing Meta Llama-3.3-70B. The framework establishes a multimodal taxonomy targeting five distinct deception vectors: generative artifact localization, chronological timeline reconstruction, rhetorical bias deconstruction, headline syntax assembly, and source interrogation. Rather than acting as an epistemic oracle, the integrated AI mentor employs a constrained Socratic dialectic framework with anti-revelation heuristics. We provide the mathematical formulation for gamified epistemic accuracy, evaluate the end-to-end architecture across low-resource network constraints, and demonstrate direct alignment with the UNESCO Media and Information Literacy (MIL) curriculum and United Nations Sustainable Development Goals (SDGs 4, 9, and 16).
Author: Rehman, Abdul
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7213927703