AI-Augmented Gamified Learning And Its Impact On Motivation In Primary English Classes

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.

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