Digital Game-Based Learning for Enhancing L2 Speaking Ability: Current Research Trends, Issues, and Future Directions

Abstract This systematic review examines 35 empirical studies published between 2015 and 2025 on digital game-based language learning (DGBLL) aimed at improving second language (L2) speaking. Across the reviewed studies, DGBLL was most consistently associated with improvements in affective readiness and speaking fluency, while evidence for accuracy was more variable, and pronunciation, complexity, and interactional competence remained comparatively underexplored. The studies most frequently attributed observed gains to a nested interplay of affective regulation, cognitive automatization, and interactional scaffolding, positioning game-mediated environments as a catalyst for participation rather than a direct accelerator of linguistic sophistication. While AI-based features remain sparsely represented, they point to a potential paradigm shift in providing adaptive scaffolding for formal accuracy. Most studies employed mixed-methods designs, but a prevalent ‘methodological mismatch’ was observed between short-term intervention durations and the protracted nature of L2 speaking development. Overall, the findings suggest that DGBLL supports specific dimensions of L2 speaking by optimizing the conditions for performance, pointing to the need for future longitudinal research to address underrepresented skills and the transformative potential of emerging technologies.