The evolving landscape: A bibliometric and visual analysis of language interventions research for children with ASD

This study conducts a multi-database bibliometric analysis to map the intellectual landscape of language intervention research for children with ASD from 2001 to 2024, seeking to identify foundational and trending topics, map collaborative networks, and trace thematic evolution, thereby offering data-driven guidance for setting research priorities, fostering international cooperation, and informing clinical practice translation.

We systematically searched Web of Science Core Collection, EBSCOhost, and PubMed. After deduplication and screening, 2720 publications were retained for bibliometric analysis using CiteSpace. Co-citation analysis, time-zone map, burst detection, and network visualization identified research clusters and temporal evolution trajectories.

Publications exhibited three distinct growth phases: initial exploration (2001-2012), accelerated expansion (2013-2017), and exponential growth (2018-2024). Ten major research clusters comprising 573 nodes demonstrated high structural validity (mean silhouette=0.835, modularity Q=0.812). Augmentative and Alternative Communication (AAC) exhibited the highest structural importance (burst=17.34, sigma=17.15), while computational methods, particularly machine learning (323 citations), showed rapid growth despite peripheral network positions (centrality=0.09), indicating they are emerging yet not central to the mainstream discourse. The United States dominated collaborative networks (betweenness=0.68, 57 connections), with emerging contributions from China, UK, and Canada.

The temporal analysis reveals that the field has successfully navigated multiple paradigm expansions, evolving from initial behavioral approaches to encompass technological and neurobiological perspectives. Five emerging frontiers warrant strategic investment: computational-clinical integration, telehealth implementation science, AI-enhanced AAC systems, neurobiological phenotyping, and community-based early detection. Future research should prioritize implementation science, foster interdisciplinary collaboration, and embed participatory principles.