The deluge of AI-generated historical content on social media creates theoretical uncertainty regarding how digital media features and personal competencies drive historical learning intentions amid cognitive risks. This study examines and deconstructs the direct and indirect causal pathways bridging digital media characteristics to history learning intentions. Integrating the response cognitive model and TAM, the variables encompass Media Exposure and Interactivity (Stimulus), Cognitive Response (Organism/Core Mediator), Perceived Ease of Use and Perceived Usefulness (Secondary Mediators), AI Literacy, and Behavioral Intention (Response). A causal quantitative survey optimized with Hair’s N-Indicator principle (three indicators per construct) was deployed and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The of result are Media exposure and interactivity exert highly significant direct effects on cognitive responses, which strongly project onto perceived ease of use and usefulness. However, direct paths from ease of use and usefulness to behavioral intention are statistically invalid, collapsing traditional sequential TAM mediation chains. Instead, learning intention is exclusively driven by cognitive responses as an absolute core mediator, while personal AI literacy remains entirely dormant. Theoretically, users evaluate synthetic history through cognitive trust and authenticity rather than system utility or technical literacy. Practically, digital historians must prioritize narrative fidelity over functional interactivity.
Publication Date: 2026-06-14