Journal Press India^®

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The rapid evolution of educational technology has opened new frontiers in how learning can be personalized and enhanced. One of the most innovative developments is the use of brain-computer interfaces (BCIs), particularly neurofeedback-based systems, which allow students to receive real-time information about their brain activity. Although neurofeedback has been studied extensively in clinical and laboratory contexts, there remains a significant research gap concerning its practical implementation in real-world classroom settings. This study addresses that gap by investigating the educational potential of neurofeedback-enhanced learning, focusing on how BCIs can improve student focus, engagement, and self-regulation in authentic classroom environments.

Anchored in constructivist and self-regulated learning theories, the study employed a mixed-methods research design involving 60 middle-school students across two pilot schools equipped with EEG-based neurofeedback headsets. Quantitative data revealed that neurofeedback contributed to a 27% increase in focused attention (Cohen’s d = 1.25), significant improvements in academic performance (Cohen’s d = 0.95), and enhanced self-regulation (Cohen’s d = 1.35) compared to controls. Qualitative interviews supported these findings, highlighting students’ increased motivation and teachers’ reports of better classroom behavior and early identification of disengagement.

The study also examines key challenges including data privacy, consent, and the digital divide, emphasizing the need for ethical frameworks and equitable implementation strategies. Furthermore, it underscores the importance of comprehensive teacher training for integrating BCIs effectively into pedagogical practices. By enabling real-time cognitive state monitoring, neurofeedback introduces the possibility of adaptive, brain-responsive curricula that adjust to learners’ needs moment by moment.

Keywords

Artificial Intelligence in Education, Personalized Learning, Educational Technology, Adaptive Learning Systems, Inclusive Education, Learning Analytics, Intelligent Tutoring Systems, Student-Centered Learning, Technology-Enhanced Learning, Individualized Instruction

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