SimStep: Chain-of-Abstractions for Incremental Specification and Debugging of AI-Generated Interactive Simulations
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912479569772544 |
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| author | Kaputa, Zoe Rajaram, Anika Feliciano, Vryan Almanon Lyu, Zhuoyue Agrawala, Maneesh Subramonyam, Hari |
| author_facet | Kaputa, Zoe Rajaram, Anika Feliciano, Vryan Almanon Lyu, Zhuoyue Agrawala, Maneesh Subramonyam, Hari |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SimStep: Chain-of-Abstractions for Incremental Specification and Debugging of AI-Generated Interactive Simulations Kaputa, Zoe Rajaram, Anika Feliciano, Vryan Almanon Lyu, Zhuoyue Agrawala, Maneesh Subramonyam, Hari Human-Computer Interaction Artificial Intelligence 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. |
| title | SimStep: Chain-of-Abstractions for Incremental Specification and Debugging of AI-Generated Interactive Simulations |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2507.09664 |