["Journal of Computer Assisted Learning, Volume 42, Issue 3, June 2026. ", "\nABSTRACT\n\nBackground\nDigital game‐enhanced vocabulary learning (DGEVL), referring to the use of commercial off‐the‐shelf games for vocabulary learning, has attracted growing scholarly interest. Although existing studies predominantly report positive effects, previous reviews have conflated different game types, restricted their scope or overlooked the processes that mediate learning outcomes. As a result, there is limited understanding of how and under what conditions DGEVL works, leaving gaps in theory and research practice.\n\n\nObjectives\nThis study aimed to examine the effectiveness of digital game‐enhanced vocabulary learning and identify the factors that facilitate or hinder its impact.\n\n\nMethods\nA meta‐analysis of 12 studies (14 samples, 765 participants) was conducted using a Bayesian random‐effects model to assess the overall impact of DGEVL. Subgroup analyses explored the influence of study, participant, intervention and assessment characteristics. To contextualise these findings, a systematic review of 25 studies, comprising 12 quantitative studies included in the meta‐analysis and an additional 13 qualitative studies, was carried out, and a thematic analysis was conducted to construct a conceptual model of the learning process.\n\n\nResults and Conclusions\nDGEVL demonstrates a strong positive effect on vocabulary learning (posterior median gwithin = 1.11, 95% credible interval (CI) [0.62, 1.61]; gbetween = 1.40, 95% CI [0.58, 2.25]), with substantial between‐study heterogeneity (τwithin = 0.56, 95% CI [0.26, 1.00]; τbetween = 0.80, 95% CI [0.26, 1.68]). These effects vary depending on factors such as the length of the intervention, learner proficiency, and the type of game played. The cyclical model conceptualises vocabulary learning as a cyclical process shaped by gameplay, language interactions and metacognitive regulation. The study highlights the need for balanced assessment techniques, prolonged interventions, micro‐longitudinal data collection, comparative designs and rigorous trials.\n\n"]