Motivating young learners to sustain effort in foreign language classrooms remains a central challenge for teachers, particularly at the primary level where attention spans are short and proficiency is emergent. Gamified learning has shown promise in making practice more engaging, yet points-and-badges alone rarely address the diverse needs of early learners. This article examines an AI-augmented approach to gamification in primary English classes that combines adaptive task sequencing, automated formative feedback, and conversational agents with narrative game mechanics. Drawing on self-determination theory, the ARCS model of motivational design, and flow theory, the study investigates whether AI supports the motivational mechanisms that underwrite durable engagement. A twelve-week quasi-experimental intervention with third- and fourth-grade pupils compared an AI-augmented, gamified English program to business-as-usual instruction across two schools. Motivation was measured with an age-adapted instrument covering interest/enjoyment, perceived competence, autonomy, and classroom attention; qualitative observations and brief learner interviews complemented the quantitative data. ANCOVA analyses controlling for baseline differences indicated significantly higher post-intervention scores in interest/enjoyment and perceived competence for the experimental group, alongside improved on-task behavior and voluntary practice time logged outside class. indings support the hypothesis that AI can turn gamified surface engagement into deeper motivational dynamics when design aligns with sound pedagogy.  

Artificial intelligence in education, gamification, primary English, motivation

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