As the digital age progresses, gamified learning emerges as a transformative force in education, especially in the realm of environmental studies. This research delves into enhancing environmental education by integrating gaming principles with educational objectives, aiming to create immersive learning experiences. However, traditional gamification approaches in environmental education often overlook the intricate group dynamics and individual learning trajectories of students. Addressing these gaps, this study focuses on optimizing task recommendations and game progression in gamified learning environments through the implementation of advanced attention mechanisms and bidirectional long-short term memory (Bi-LSTM) neural networks. These technologies enable precise predictions of students’ evolving preferences and facilitate the customization of learning tasks, thereby enriching the educational experience. Furthermore, the study explores adaptive strategies for modifying game progression based on real-time learning outcomes, ensuring that educational content remains challenging yet attainable. The insights gained from this research provide a robust theoretical framework and practical tools for effectively employing gamification strategies in environmental education, thereby fostering deeper student engagement and a profound understanding of environmental issues. This study explores the adaptive adjustment of progress plans in gamified learning by focusing on identifying deviation intensity, processing priorities, and determining the appropriate range of deviation values. These findings not only optimize the gamified learning structure but also enhance the personalized instructional effectiveness of educational games.