Exploring the Impact of Gamified Artificial Intelligence–Driven English Vocabulary Learning Systems on Learner Retention and Motivation

The growing development of digital learning platforms has posed an increased demand on gamified and artificial intelligence-based methods of enhancing English vocabulary learning. However, existing studies often treat gamification and AI as loosely pair components, relying on static game mechanics or post-hoc analytics that limit personalization, adaptability, and long-term learning impact. To address these limitations, this study proposes the Gamified AI-Driven Vocabulary Retention and Motivation Enhancer (GAI-VRME), an adaptive learning framework that integrates machine-learning–based learner modeling, real-time difficulty calibration, and adaptive gamification strategies. In contrast to the previous systems, GAI-VRME can dynamically regulate the complexity of the task, the frequency of feedback and the sequence of rewards according to the performance and the motivational state of a specific learner, and can thus be constantly customized to the individual level as the process of learning progresses. The implementation and empirical assessment of the framework were conducted with the help of Python, TensorFlow, and Jupyter Notebook and Teaching-Learning Gamification Dataset of Mendeley Data. Mixed method analysis of vocabulary retention with paired t-tests and sentiment-analysis-based motivation modelling was used. The experimental outcomes show that GAI-VRME has much higher predictive accuracy, vocabulary retention, and learner motivation than the traditional gamified systems. These findings provide empirical evidence that deeply integrated AI-driven adaptive gamification, jointly optimizing cognitive retention and affective engagement, offers a scalable and pedagogically robust solution for modern digital vocabulary learning environments.