Transforming STEM Education in Under-Resourced Contexts: A Conceptual Framework for AI-Supported OEP/Rs and Inquiry-Based Learning

While digital innovation is rapidly transforming global classrooms, the pedagogical and resource divide in under-resourced STEM environments remains a critical barrier to equitable education. This conceptual paper aims to examine design-based practices (DBPs) for implementing Artificial Intelligence (AI)-supported Open Educational Resources and Practices (OEP/Rs) to promote inquiry-led teaching in STEM education within under-resourced contexts. Guided by the Inquiry-Based Learning (IBL) model, it explores how AI-supported digital simulations can be designed to enhance learner engagement and conceptual understanding. The methodology employs a critical and integrative literature review anchored in the 5E instructional framework to synthesize pedagogical benefits, implementation barriers, and design principles. The results present three adaptable teaching scenarios (natural science, technology, and mathematics) demonstrating how generative AI tools create curriculum-aligned, multilingual, and contextually relevant digital simulations. These findings show that AI-generated simulations successfully transform abstract concepts into interactive experiences, helping overcome infrastructural, linguistic, and contextual constraints in rural classrooms. The main contribution of this research lies in establishing a novel conceptual framework that bridges generative AI co-design with open pedagogy. It offers a scalable, cost-effective pathway for digital inclusion and equitable STEM education, providing actionable guidelines for educators to design localized digital learning objects without requiring advanced programming.