Date
Publisher
arXiv
When designing a program, both novice programmers and seasoned developers alike often sketch out -- or, perhaps more famously, whiteboard -- their ideas. Yet despite the introduction of natively multimodal Generative AI models, work on Human-GenAI collaborative coding has remained overwhelmingly focused on textual prompts -- largely ignoring the visual and spatial representations that programmers naturally use to reason about and communicate their designs. In this proposal and position paper, we argue and provide tentative evidence that this text-centric focus overlooks other forms of prompting GenAI models, such as problem decomposition diagrams functioning as prompts for code generation in their own right enabling new types of programming activities and assessments. To support this position, we present findings from a large introductory Python programming course, where students constructed decomposition diagrams that were used to prompt GPT-4.1 for code generation. We demonstrate that current models are very successful in their ability to generate code from student-constructed diagrams. We conclude by exploring the implications of embracing multimodal prompting for computing education, particularly in the context of assessment.
Study at a glance
- Question: Can generative AI accurately interpret student-drawn program decomposition diagrams to generate functional code?
- Setting and data: 133 students in a large introductory computer science course at a research university in the United States.
- Method: Case study analyzing student hand-drawn diagrams and evaluating AI-generated code against human-coded benchmarks.
Why it matters
Collaborative coding with generative AI remains heavily focused on text prompts, overlooking the visual diagrams programmers naturally sketch to reason about and communicate software design.
Key findings
- GPT-4.1 successfully extracted the defined function structure from student-drawn decomposition diagrams in 81.2% of cases.
- The number of functions generated by the model was strongly correlated with human-identified functions (r = 0.776).
- When deviations occurred, models consistently produced more functions than humans identified, mainly due to misinterpreting annotations, conflating inputs with functions, or adding unprompted helper functions.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Study design
