Deep learning prediction of preschool language development trajectories: Multimodal modeling for early educational environments

Early identification of language development delays in preschool children enables timely intervention during critical developmental windows, yet existing screening tools rely on periodic snapshots that miss the dynamic, trajectory-based nature of language acquisition. This study presents a multimodal deep learning framework - a Bidirectional Long Short-Term Memory network with multi-head attention - for predicting individual language development trajectories from birth to 24 months using acoustic, linguistic, and behavioral feature streams collected in naturalistic early educational environments. Trained on 6349 longitudinal assessments from 847 children across three collection sites, the model achieves a mean absolute error of 4.2 score points, R² = 0.93, and an AUC-ROC of 0.94 for language delay classification. Ablation experiments confirm that multimodal fusion outperforms any single modality, with linguistic features contributing the largest marginal gain. The model accurately identifies children with language delay risk at 12 months with 84% sensitivity and 92% specificity, outperforming standard clinical screening instruments on specificity while maintaining comparable sensitivity. Subgroup analyses confirm consistent performance across socioeconomic, bilingual, and gender-based subgroups. Feature importance analysis reveals that early acoustic features dominate prediction in the first six months, while linguistic and behavioral features grow in importance across development. Practical implementation guidelines are provided for deployment in preschool and early educational settings.