ql-align-justify">Background: Inclusion systems of special education have long struggled to provide individualized, transparent and equitable learning conditions. Traditional reactive models are not usually able to support learner variability, which is one of the roots of a global educational divide whereby millions of children with disabilities have inadequate access to quality education. As promising as an adaptive learning and intelligent tutoring tool may be, the black-box nature of Artificial Intelligence (AI) tends to erode trust and cast doubt on ethical aspects of automated decision-making. Explainable Artificial Intelligence (XAI) overcomes these drawbacks by improving the interpretability of algorithm processes. At the same time, Universal Design for Learning (UDL) offers a proactive pedagogical model that stresses variability of learners, their agency, and elimination of systemic barriers as further supported in the UDL 3.0 guidelines.

Purpose: This review synthesizes the literature on XAI and UDL, demonstrating their complementary strengths and proposing an integrated XAI-UDL system to support the inclusive special education system. It also analyzes the applicability and implication of educational and research ecosystem of the United States in the promotion of this integrated system.

Procedure: A systematic review of the literature was performed using Scopus, Web of science, ERIC, Pubmed and Google scholar to retrieve 2019-2026 publications. Search terms were: Explainable AI, Universal Design for Learning, special education, inclusive learning. The peer-reviewed journal articles, conference proceedings, and empirical studies were subjected to rigorous quality appraisal criteria, which resulted in a final dataset of 165 sources.

Results: The synthesis demonstrates strong synergistic benefits from integrating XAI and UDL. XAI offers transparency and interpretability, builds trust, accountability, and learner agency, and UDL provides the pedagogical framework that is required to transform AI-driven insights into meaningful instructional practices. Empirical data show that digital literacy, learner self-efficacy, and system usability among students with disabilities greatly improved. Moreover, the educational and research ecosystem in the United States, which is characterized by strong policy frameworks, interdisciplinary research funding, technological innovation, and leadership on inclusive education, provides a highly conducive environment to the development, validation and large-scale application of the XAI-UDL framework. Federal requirements in favor of inclusive education, effective academia-industry partnership, and investment in the study of AI helps the ethical implementation of explainable systems in educational practice.

Conclusion: The proposed XAI–UDL framework offers a viable pathway toward transparent, adaptive, and inclusive learning environments. Its congruency with the developed educational system and research capabilities of the United States further underpins its scalability and universal applicability. This synthesis offers crucial advice to researchers, educators, and policymakers to utilize AI in a responsible manner and enhance equity and inclusion in special education systems.

An Integrated Explainable AI and Universal Design for Learning (UDL) Framework for Inclusive Special