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arXiv · 2507.09664

SimStep: Chain-of-Abstractions for Incremental Specification and Debugging of AI-Generated Interactive Simulations

Zoe Kaputa·Anika Rajaram·Vryan Almanon Feliciano·Zhuoyue Lyu·Maneesh Agrawala·Hari Subramonyam
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Abstract

Programming-by-prompting with generative AI offers a new paradigm for end-user programming, shifting the focus from syntactic fluency to semantic intent. This shift holds particular promise for non-programmers such as educators, who can describe instructional goals in natural language to generate interactive learning content. Yet in bypassing direct code authoring, many of programming's core affordances - such as traceability, stepwise refinement, and behavioral testing - are lost. We propose the Chain-of-Abstractions (CoA) framework as a way to recover these affordances while preserving the expressive flexibility of natural language. CoA decomposes the synthesis process into a sequence of cognitively meaningful, task-aligned representations that function as checkpoints for specification, inspection, and refinement. We instantiate this approach in SimStep, an authoring environment for teachers that scaffolds simulation creation through four intermediate abstractions: Concept Graph, Scenario Graph, Learning Goal Graph, and UI Interaction Graph. To address ambiguities and misalignments, SimStep includes an inverse correction process that surfaces in-filled model assumptions and enables targeted revision without requiring users to manipulate code. Evaluations with educators show that CoA enables greater authoring control and interpretability in programming-by-prompting workflows.

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Zoe Kaputa, Anika Rajaram, Vryan Almanon Feliciano, Zhuoyue Lyu, Maneesh Agrawala, Hari Subramonyam. 2025-07-13. SimStep: Chain-of-Abstractions for Incremental Specification and Debugging of AI-Generated Interactive Simulations. https://arxiv.org/abs/2507.09664

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