Universal Design for Learning (UDL) is increasingly recognized as a critical framework for promoting equity and accessibility in Science, Technology, Engineering, and Mathematics (STEM) education. In this review, we synthesize research published between 2002 and 2025 on UDL in STEM education, with attention to technology-enhanced learning environments. Searches were conducted in Scopus, Web of Science, Education Resources Information Center (ERIC), and IEEE Xplore. After title/abstract screening and full-text assessment, 13 studies were included: 11 empirical studies and 2 theoretical/conceptual papers. Across the empirical studies, the findings suggest promising but heterogeneous evidence that UDL-informed STEM practices may support access, engagement, self-regulation, persistence, and inclusive participation, while evidence for standardized achievement gains remains more mixed. The synthesis also highlights recurring design considerations, including multimodal representation, flexible participation, scaffolded learning, and social–emotional support. We conclude that UDL offers practical guidance for inclusive STEM education, particularly when learning environments are designed to support cognitive access, emotional engagement, collaboration, and diverse learner needs.
Chrysovalantis Kefalis, C. Skordoulis, Chara Papoutsi et al.· Computers· 0 citations
Conversational artificial intelligence (AI) has shown potential to support knowledge translation, personalized education, and access to health-related information. However, applications in neurodevelopmental care remain largely focused on screening and assessment, while conversational systems tailored to occupational therapy are scarce. Moreover, general-purpose generative AI may produce inaccurate, insufficiently contextualized, or clinically inappropriate responses, highlighting the need for evidence-based, domain-specific systems with robust safety mechanisms. This study describes the protocol for the co-design, development, and preliminary evaluation of a conversational AI system designed to support occupational therapists and parents of children aged 5–12 years with neurodevelopmental disorders in Greece. The system is intended as an educational and decision-support resource and will not replace diagnosis, clinical judgment, or individualized intervention. A co-designed, multi-phase, mixed-methods proof-of-concept design will be adopted, informed by the Medical Research Council framework, Design Science Research, user-centered design principles, and the CeHRes Roadmap 2.0. Development will include evidence synthesis, stakeholder needs assessment, knowledge base construction, iterative prototype development, and expert, technical, safety, and user evaluation. The system will integrate a curated occupational therapy knowledge base, retrieval-augmented generation, role-specific prompting, source verification, and layered safety guardrails. Expected outputs include a stakeholder-informed Greek-language minimum viable product and a transparent framework linking evidence, user requirements, technical design, and evaluation criteria. Preliminary evaluation will assess factual accuracy, evidence concordance, occupational therapy relevance, clinical appropriateness, safety, usability, acceptability, and perceived usefulness. This protocol provides a reproducible foundation for developing clinically relevant conversational AI in occupational therapy and for future feasibility and effectiveness studies.
Pantelis Pergantis, N. Bardis, Charalabos Skianis et al.· Brazilian Journal of Science· 0 citations
