AI and Educational Data Analytics in IEP Implementation: A Scoping Review of U.S. K–12 Education
Ava-Gay Annakaye Morgan
*
Department of Curriculum and Instruction, College of Education, Texas Southern University, Houston, TX, USA.
*Author to whom correspondence should be addressed.
Abstract
The Individuals with Disabilities Education Act (IDEA) requires that millions of students with disabilities in U.S. public schools receive individualized services through Individualized Education Programs (IEPs). However, persistent challenges in implementation fidelity, progress monitoring, and compliance continue to affect outcomes. This scoping review examines the emerging role of artificial intelligence (AI) and educational data analytics in addressing these challenges. Guided by the Arksey and O’Malley framework and reported in accordance with PRISMA-ScR guidelines, a systematic search of five electronic databases (ERIC, PsycINFO, Scopus, Web of Science, and Education Source) was conducted for studies published between January 2015 and December 2025. A total of 24 sources were included, comprising empirical studies (n = 8), literature reviews (n = 6), policy and grey literature (n = 5), conceptual papers (n = 3), and training modules (n = 2). Findings were organized across five thematic domains: AI-assisted IEP development, data analytics for progress monitoring, compliance and fidelity tracking, educator workload, and ethical considerations. Results indicate increasing adoption of AI tools alongside significant gaps in empirical validation, bias mitigation, and data privacy safeguards. The study proposes a conceptual framework and outlines implications for research, policy, and practice to support effective and equitable IEP implementation.
Keywords: Artificial intelligence, educational data analytics, individualized education programs, special education, IDEA compliance, IEP fidelity, progress monitoring, scoping review