Eric Yocam, Varghese Mathew Vaidyan, Paris Kalathas · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.28859 · cited by 0 works (OpenAlex) · open-access PDF
K-12 स्कूलों में कृत्रिम बुद्धि को अपनाना छात्रों, शिक्षकों और प्रशासनियों के लिए मार्गदर्शन से आगे बढ़ गया है। मौजूदा एआई-लेखन ढांचे समझ और उपयोग पर जोर देते हैं; साइबर सुरक्षा ढांचे एआई-विशिष्ट खतरों से पहले होते हैं, और न ही एआई सुरक्षा पढ़ाई को अपने अधिकार में एक योग्यता डोमेन के रूप में परिभाषित करते हैं। हम स्कूल पारिस्थितिकी तंत्र को फैलाने वाले छह आयाम के एआई सुरक्षा पढ़ाई (AISL) ढांचे की पेशकश करते हैं, अपने तकनीकी आयामों को सार्वजनिक एआई-सुरक्षा मानदंडों और एक पुनरावृत्ति योग्य बहु-सीन विरोधाभासी मूल्यांकन में जमीन देते हैं, और अमेरिकी शिक्षकों और वयस्कों से राष्ट्रीय
Machine translation (Meta M2M100); the English original follows.
Adoption of artificial intelligence in K-12 schools has outpaced guidance for students, teachers, and administrators. Existing AI-literacy frameworks emphasize understanding and use; cybersecurity frameworks predate AI-specific threats, and neither defines AI security literacy as a competency domain in its own right. We propose an AI Security Literacy (AISL) framework of six dimensions spanning the school ecosystem, ground its technical dimensions in public AI-safety benchmarks and a reproducible multi-seed adversarial evaluation, and ground its attitudinal dimensions in nationally representative survey micro-data from U.S. teachers and adults. Only 34.5% of teachers report a school AI policy, and teachers see their own AI use as easing their work but students' use as complicating it; on the technical side, a reported robustness figure depends as much on the attack and the particular trained model as on the system evaluated. NeuralFortify, a browser-based K-12 platform built on real AI models, is presented as an illustrative operationalization.
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Topics: Adversarial system, Robustness (evolution), Computer science, Literacy, Domain (mathematical analysis), Work (physics)
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