Low-code platforms are typically designed for industry productivity, yet their architectural and interactive qualities make them powerful for computing education. This demo presents Lightcodepedia, a distributed, browser-based platform offering an interactive playground for introducing learners to core computing concepts—abstraction, state, validation, and automation—through progressively disclosed layers.

Learners begin with interactive widgets that let them build and test behavior visually in a self-directed, non-linear way. Only later do they discover the underlying living, executable artifacts—specifications, models, tests, and code—with collaborative support, revealing how each element contributes to the system’s model-driven architecture. This outside-in progression helps learners move from interaction to reflection, building confidence before encountering formal syntax.

The platform runs on autonomous browser nodes that can operate independently or in collaboration. Nodes support transparent version control and user-defined access, allowing applications and data to be open-source or private, public or local. Each node serves as a self-contained environment for creating, modifying, and testing applications, with UI editors automatically available for any object. Embedded testing and “living documentation” keep models and execution aligned, while AI copilots provide optional scaffolding for exploration.

During the demo, attendees will follow a brief progression from interactive widgets to executable models and code, discovering how distributed, model-driven low-code systems can serve as both approachable and rigorous learning environments for broadening participation in computing.

Michel Zam, PhD, is an academic and practitioner in computer science working across higher education, industry, and adaptive digital systems. His work centers on software architecture, model-driven engineering, and agile approaches to development, with an emphasis on structuring environments that support learning, coordination, and progressive autonomy. He designs and facilitates scalable, gamified frameworks for knowledge acquisition and practice—applicable to audiences ranging from student cohorts to professional teams, and extensible to computational agents operating within hybrid human–digital ecosystems. In parallel, he builds tools that embody these principles, enabling engaging, practice-oriented, and continuously evolving learning experiences. These frameworks and tools support value-driven development and the continuous evolution of both practices and models.