Multimodal Diagnosis of AI Literacy Among University Teachers and Students under the Background of Big Data

Abstract

In the macro-context of artificial intelligence reshaping social production and daily life, AI literacy has become a core competency and key competitive edge for individuals adapting to survival and development in the intelligent era. As educational evaluation shifts from a single outcome-oriented approach to all-process procedural diagnostics, traditional assessment paradigms have revealed certain limitations: most AI literacy assessments heavily rely on static scales, making it difficult to penetrate the deep mechanisms of individual cognitive processing. In view of this, constructing a multimodal diagnostic model through systematic thought experiments and theoretical deduction. The “Neuro-Behavior-Capability” ternary dynamic model aims to bridge three dimensions—neuroscience, behavioral patterns, and ability performance—systematically outlining the deep underlying mechanisms behind the formation of AI literacy. The model break through the surface-level limitations of current assessments and lay a theoretical foundation for the development of intelligent, precision-driven AI literacy assessment systems.