Thematic mapping of autism spectrum disorder research using machine learning and LDA: trends, patterns, and future directions

This study presents a large-scale thematic analysis of educational research on Autism Spectrum Disorder (ASD) published between 1981 and 2024, utilizing machine learning-based Latent Dirichlet Allocation (LDA) topic modeling. By systematically analyzing 1,654 articles retrieved from the Web of Science and Scopus databases, the research identifies ten principal themes shaping the field. The findings reveal that “ASD and Education: Teacher Perspectives, Inclusive Education Policies, and Practices” constitutes the most dominant research area. Furthermore, there is a rapidly accelerating focus on “ASD and Educational Technologies,” specifically regarding digital tools and artificial intelligence-assisted interventions. The study highlights critical gaps in the current literature, particularly the lack of longitudinal research on the long-term outcomes of AI and robotics, as well as the ongoing challenges teachers face due to inadequate specialized training and program deficiencies. Ultimately, this research underscores the vital importance of enhanced teacher training, mindful technology integration, and robust inclusive policies to develop more effective, evidence-based educational strategies for children with ASD.