Interdisciplinary Integration of Knowledge Construction Based on the ARCS Motivational Model in AI-Driven Gamified Learning Environments

The rapid advancement of artificial intelligence (AI) technology is fundamentally reshaping educational paradigms. AI-driven gamified learning environments demonstrate unique potential in facilitating interdisciplinary knowledge construction through two key mechanisms: (1) the strategic integration of game mechanics into pedagogical processes, and (2) the systematic application of AI capabilities to transcend traditional disciplinary boundaries. This study establishes a theoretical framework synthesizing gamification principles with interdisciplinary education theory, with particular emphasis on the ARCS (Attention, Relevance, Confidence, Satisfaction) motivation model and intelligent educational agents. Our proposed framework employs AI algorithms to generate adaptive learning scenarios and multimodal interaction systems designed to achieve dual objectives: cognitive engagement through attention-capturing mechanics and pedagogical alignment through personalized content delivery. The research further develops a three-phase implementation strategy for interdisciplinary knowledge integration within gamified contexts: (1) contextual bridging across disciplines, (2) schema reconstruction through problem-based challenges, and (3) meta-cognitive reinforcement via intelligent feedback systems. Empirical evidence from our experimental implementation reveals statistically significant improvements in both learner engagement metrics (p<0.01, Cohen's d=1.2) and knowledge transfer scores (p<0.05, η<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.32), demonstrating the framework's efficacy in operationalizing motivation theory while addressing contemporary educational challenges. These findings not only advance theoretical understanding of technology-enhanced learning but also provide actionable insights for designing next-generation educational systems that effectively bridge disciplinary divides.