Abstract: Attention has emerged as a critical determinant of learning outcomes in an increasingly digital educational landscape. Traditional assessment methods, however, inadequately capture the uncertainty and dynamic fluctuations characteristic of attention data. We developed a Dynamic Nonlinear Grey Relational Analysis (DN-GRA) model integrated with electroencephalography (EEG)neurofeedback technology to enable real-time detection and prediction of attention patterns, thereby facilitating personalized educational interventions. A 12-week randomized controlled trial with 200 primary school students (grades 3-4) revealed that the experimental group significantly outperformed controls across multiple dimensions: attention stability increased 175%, sustained attention duration improved 120%, and average attention values rose 129% (all p < 0.001). Students also demonstrated substantial gains in memory capacity (28.5%), logical reasoning (30.1%), and overall academic performance (effect size d = 0.82). Grey Relational Analysis validation confirmed the effectiveness of personalized intervention strategies, establishing a practical framework for integrating grey system theory (GST) with neurotechnology to advance educational equity and promote sustainable learning outcomes.

Key words: Sustainable education, Attention cultivation, Grey system theory, Personalized learning, Educational technology