Research on the Improvement of Children’s Attention Through Binaural Beats Music Therapy in the Context of AI Music Generation

In this study, we explored the potential of Binaural Beats Music Therapy (BBMT) augmented with AI-generated music to enhance attention in children diagnosed with Attention Deficit Disorder (ADD). Utilizing a 2 \(\,\times \,\) 2 mixed experimental design, we differentiated between traditional and AI-generated music (between-subjects) and incorporated vs. excluded BBMT (within-subjects). We engaged 60 children, averaging 8 years of age, and diagnosed via the SNAP-IV scale. These children were randomly assigned to either the Original Music or AI Music groups, each consisting of 30 participants. Over a 4-week period, with 30-min sessions conducted five days a week, we gauged Heart Rate Variability (HRV) data before and after subjecting each group to their allocated music sessions, integrating BBMT where necessary. Subsequent analysis revealed a marked increase in attention post-intervention. Both music methodologies, when amalgamated with BBMT, significantly bolstered attention levels (P < 0.05). Intriguingly, AI music’s efficacy paralleled that of its traditional counterpart, underscoring the maturity of AI in music generation and its viability for routine therapeutic application.