Date
Publisher
arXiv
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.
Study at a glance
- Question: How can an AI tool help teachers create, test, and refine interactive educational simulations without writing code?
- Setting and data: Educators
- Method: Software development and user evaluations with educators
Why it matters
Programming by prompting lets educators describe instructional goals to generate interactive learning content. However, bypassing direct code authoring loses core programming affordances such as traceability, stepwise refinement, and behavioral testing.
Key findings
- In evaluations with educators, structured AI checkpoints gave teachers greater control and clarity when creating interactive learning simulations using prompts.
- The tool reveals hidden AI assumptions, allowing educators to make targeted revisions and fix simulation misalignments without manipulating computer code.
- Bypassing code authoring with natural language prompts causes educators to lose essential programming capabilities, including step-by-step refinement and behavioral testing.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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