The effects of perceived technical explanation and analogical explanation on learning behavioral intention among K-12 students with different subject preferences in AI-assisted learning.

Abstract

The proliferation of Generative AI (GAI) in K-12 education makes AI Explanation (AIE) style a critical yet underexplored factor in learning engagement. Grounded in Dual-Process Theory and Cognitive Load Theory, this study examines how Perceived Technical Explanation (PTE) and Perceived Analogical Explanation (PAE) affect Learning Behavioral Intention (LBI) via Germane Cognitive Load (GCL), Self-Efficacy (SE), and Learning Interest (LI), and whether Subject Preference (SP) moderates these effects. Using a single-group dual-stimulus perception design, 258 Grade 8 students across three Chinese middle schools simultaneously received both AIE styles and rated their perceptions under joint exposure conditions. Data were analyzed via PLS-SEM. PTE significantly predicted LBI directly and indirectly via SE. PAE influenced LBI only through LI. GCL did not predict LBI. SP moderated the PTE-LBI path but not PAE-LBI. Findings offer preliminary evidence that subject preference conditions the effectiveness of technical explanation on behavioral intention, suggesting that AIE style differentiation warrants consideration in GAI system design for K-12 learners.