SimStep: Human-in-the-Loop Authoring of Interactive Educational Simulations Through Task-Level Abstractions

Generative AI enables educators to create interactive learning content by describing goals in natural language. However, without programming affordances such as traceability, refinement, and debugging, teachers struggle to align simulations with learners’ needs, refine them step by step, or verify that they reflect intended learning concepts. We propose a task-level abstraction approach that structures authoring as a sequence of representations, mirroring how teachers plan lessons and providing checkpoints for specification, inspection, and refinement. We instantiate this approach in SimStep, an authoring environment that scaffolds simulation design with four abstractions, including Concept Graph, Scenario Graph, Learning Goal Graph, and UI Graph, and introduces an inverse correction process to revise hidden model assumptions without requiring code manipulation. A technical evaluation shows that these abstractions preserve fidelity across transformations, while a user study with educators demonstrates their effectiveness in authoring simulations. Our work reframes AI-assisted programming as human–AI co-authoring through structured, domain-aligned abstractions.