Abstract
Digital Game-Based Language Learning (DGBLL) is an important approach in language education. Yet, the role of corrective feedback, which helps connect gameplay with language learning and development by guiding learners towards improved performance, remains unclear. This systematic review therefore examined how different types of feedback influence learning outcomes in DGBLL environments. Guided by the PRISMA 2020 framework and Van der Kleij’s technology-enhanced feedback framework, five peer-reviewed studies from the Web of Science and Scopus databases were analysed. The findings showed that immediate corrective feedback and multimodal feedback were the most used approaches, while metalinguistic and AI-driven feedback received limited attention. Overall, corrective feedback was found to improve vocabulary, grammar, learner engagement, and motivation. Its effectiveness depended on its relevance to learning objectives and the quality of information provided. The review also identified several research gaps, including the limited use of adaptive feedback, the lack of long-term studies, and insufficient attention to listening and reading skills. These findings provide useful insights for researchers, educators, and game designers seeking to develop more effective feedback systems in DGBLL environments.