This Research Topic of Frontiers in Education invites empirical research that examines how artificial intelligence (AI) tools can be designed, implemented, and evaluated to advance Universal Design for Learning (UDL) principles and foster inclusive educational environments. We welcome studies that explore AI’s potential to reduce barriers, increase engagement, and promote equitable learning opportunities for students with learning differences, special educational needs, and diverse learner profiles.

Artificial intelligence (AI) is transforming education, offering new ways to address learner variability and reduce barriers to participation. Yet, evidence on how AI can be effectively aligned with Universal Design for Learning (UDL) principles to build truly inclusive environments remains limited and fragmented. This Research Topic seeks to fill that gap by gathering empirical studies that examine AI’s potential to enhance accessibility, engagement, and learning outcomes for students with diverse needs, including those with learning differences and special educational needs.

Foregrounding UDL ensures that AI tools and systems are designed for equity from the start rather than retrofitted for inclusion. We invite contributions that identify suitable practices acknowledging learner differences and that address the challenges of adapting large language models (LLMs) with appropriate “sidecars” to meet specialized needs. Just as importantly, we call for critical perspectives that highlight cautionary uses of AI, ensuring readers are informed not only of its promise but also of risks and unintended consequences for vulnerable populations.

By integrating cross-disciplinary, cross-cultural, and cross-national perspectives, this collection will advance theory, practice, and policy, shaping innovations that responsibly leverage AI to create inclusive, high-quality education for all.

Submissions should be grounded in rigorous empirical methodology—including quantitative, qualitative, mixed methods, single-case design, or design-based research—and provide actionable insights for educational practice and/or policy. Interdisciplinary and multidisciplinary approaches are encouraged, as are cross-cultural and cross-national studies that broaden the global evidence base for inclusive education. Manuscripts may address, but are not limited to:

- AI-enhanced instructional strategies aligned with UDL principles to support learner variability.

- Empirical evaluations of AI tools improving accessibility, engagement, or persistence in learning.

- Studies connecting AI use to measurable learning outcomes across diverse populations and contexts.

- Innovative AI applications for differentiated instruction and personalized learning.

- Cross-national comparisons of AI-supported UDL implementation and impact.

Are you interested in contributing a manuscript to this Research Topic?

Artificial Intelligence and Universal Design for Learning: Empirical Pathways to Inclusive Education