Closing the Collaboration Gap: Scaling AI Architectures into Real-World STEM Learning Environments

This research explores the transformative potential of Artificial Intelligence (AI) in STEM education, focusing on its capacity to measure and enhance transversal competencies. While STEM models successfully link conceptual knowledge with practical applications, quantifying 'invisible skills'—such as resilience, computational thinking, and collaborative leadership—remains a complex methodological challenge. This paper presents a framework grounded in the author’s experience mentoring robotics teams and conducting PhD research at City, University of London. By leveraging Multimodal Learning Analytics, Predictive Modeling, and AI-driven adaptive scaffolding, the study proposes a transition from subjective observation to an evidence-based diagnostic system. The findings underscore the importance of aligning AI integration with international equity agendas, such as UNICEF 2025, to foster gender equality and reduce academic lag. Ultimately, the research aims to empower educators as architects of dynamic, inclusive, and scalable educational ecosystems through the democratization of AI tools