Investigating the influencing factors and structural pathways of artificial intelligence literacy from a triadic reciprocal perspective: an ISM-MICMAC modeling approach

Purpose Building on the triadic reciprocal perspective of cognition, competency and environment, this study investigates the influencing factors and interaction pathways of AI literacy, aiming to provide theoretical insights and practical guidance for systematically cultivating AI literacy. Design/methodology/approach A hybrid systems methodology integrating interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) was employed. ISM was utilized to delineate the hierarchical structure of AI literacy influencing factors, clarifying the progressive influence pathways from external environmental shaping to individual cognitive internalization and behavioral manifestation. MICMAC complemented this by quantifying the driving power and dependence level of factors, thereby elucidating a systemic mechanism spanning root-level drivers, intermediate moderators and dependent responses. Findings AI literacy development constitutes a multidimensional interactive process governed by dual dynamics of “intrinsic drivers-extrinsic enablers:” individual-level competencies and behaviors stem predominantly from intrinsic cognitive and affective motivations. Cognitive-affective transformations rely on guided reinforcements from technological and environmental stimuli. Competency factors manifest as observable literacy outcomes and cognitive factors mediate transitional interactions, while technological and environmental factors form the foundational motivational architecture. These hierarchical dimensions operate independently yet achieve systemic integration through “cognition-behavior” feedback loops and “environment-individual” interaction mechanisms, collectively shaping public comprehension and application of AI technologies. Originality/value This study constructs a system of factors influencing AI literacy based on the triadic reciprocal perspective, transcending the limitations of fragmented analyses in prior research. It offers a holistic perspective for understanding and systematically investigating AI literacy. Furthermore, by deconstructing the hierarchical architecture and multilevel interaction mechanisms of influencing factors, it addresses structural analytical gaps in existing literature, providing critical theoretical underpinnings for future scholarly inquiry and practical interventions.