# AI-driven gamification and metacognitive strategy development: a study of primary school English vocabulary learning from a self-regulated learning perspective

**Authors:** Hongling Wu, Xingting Wang

PMC · DOI: 10.3389/fpsyg.2026.1692949 · Frontiers in Psychology · 2026-03-09

## TL;DR

This study shows how AI-powered gamified learning helps primary students learn English vocabulary by improving their self-regulation and metacognitive skills.

## Contribution

The study provides a meta-analysis showing how AI-driven gamification enhances vocabulary learning through metacognitive strategy development.

## Key findings

- AI-driven gamified learning significantly improves vocabulary acquisition (Cohen’s d = 0.72).
- Adaptive feedback mechanisms enhance metacognitive awareness and learning persistence.
- Cultural context, technology acceptance, and teacher support moderate AI gamification outcomes.

## Abstract

With the rapid advancement of artificial intelligence technology in education, AI-driven gamified learning environments offer innovative pathways for primary English vocabulary instruction. Grounded in self-regulated learning theory, this study systematically examines the mechanisms through which AI-personalised gamified environments influence metacognitive strategy development in primary pupils’ English vocabulary acquisition. Through a meta-analysis of 45 empirical studies conducted between 2019 and 2024, the research reveals that AI-driven gamified learning environments demonstrate significant effects in promoting vocabulary acquisition (Cohen’s d = 0.72), enhancing metacognitive awareness (Cohen’s d = 0.68), and improving learning persistence (Cohen’s d = 0.64). Adaptive feedback mechanisms played a crucial mediating role, effectively fostering learners’ self-regulation capabilities by dynamically adjusting learning pathways and delivering personalised guidance. The study further revealed the significant moderating effects of cultural context, technology acceptance, and teacher support on AI gamification outcomes. This research provides theoretical foundations and practical guidance for AI applications in language education, holding considerable value for advancing the design of personalised language learning environments.

## Full text

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## Figures

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## References

47 references — full list in the complete paper: https://tomesphere.com/paper/PMC13006607/full.md

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Source: https://tomesphere.com/paper/PMC13006607