# BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks

arXiv: https://arxiv.org/abs/2404.07387 · HTML: https://mlsanity.com/paper/2404.07387/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 81 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [DynaVis: Dynamically Synthesized UI Widgets for Visualization Editing](https://mlsanity.com/paper/2401.10880/) (2024)
- [Investigating and Designing for Trust in AI-powered Code Generation Tools](https://mlsanity.com/paper/2305.11248/) (2023)
- [On the Design of AI-powered Code Assistants for Notebooks](https://mlsanity.com/paper/2301.11178/) (2023)
- [How Readable is Model-generated Code? Examining Readability and Visual Inspection of GitHub Copilot](https://mlsanity.com/paper/2208.14613/) (2022)
- [Ivie: Lightweight Anchored Explanations of Just-Generated Code](https://mlsanity.com/paper/2403.02491/) (2024)
- [mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks](https://mlsanity.com/paper/2009.10643/) (2020)
- [NL2INTERFACE: Interactive Visualization Interface Generation from Natural Language Queries](https://mlsanity.com/paper/2209.08834/) (2022)
- [How Interest-Driven Content Creation Shapes Opportunities for Informal Learning in Scratch: A Case Study on Novices' Use of Data Structures](https://mlsanity.com/paper/2203.11479/) (2022)

## Built on by (most cited first, 12 of 12)

- [Generative Interfaces for Language Models](https://mlsanity.com/paper/2508.19227/) (2025)
- [A Framework for Situating Innovations, Opportunities, and Challenges in Advancing Vertical Systems with Large AI Models](https://mlsanity.com/paper/2504.02793/) (2025)
- [AlignUI: A Method for Designing LLM-Generated UIs Aligned with User Preferences](https://mlsanity.com/paper/2601.17614/) (2026)
- [HiLDe: Intentional Code Generation via Human-in-the-Loop Decoding](https://mlsanity.com/paper/2505.22906/) (2025)
- [Auditing and Controlling AI Agent Actions in Spreadsheets](https://mlsanity.com/paper/2604.20070/) (2026)
- [AgentLens: Adaptive Visual Modalities for Human-Agent Interaction in Mobile GUI Agents](https://mlsanity.com/paper/2604.20279/) (2026)
- [Beyond Autocomplete: Designing CopilotLens Towards Transparent and Explainable AI Coding Agents](https://mlsanity.com/paper/2506.20062/) (2025)
- [The Command Line GUIde: Graphical Interfaces from Man Pages via AI](https://mlsanity.com/paper/2510.01453/) (2025)
- [Beyond Chat and Clicks: GUI Agents for In-Situ Assistance via Live Interface Transformation](https://mlsanity.com/paper/2604.14668/) (2026)
- [Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph](https://mlsanity.com/paper/2604.18883/) (2026)
- [Plans Work in Mysterious Ways: Evaluating a Plan Mode for Spreadsheet Agents](https://mlsanity.com/paper/2607.23670/) (2026)
- [Gricea: An Open Science Platform for Conversational AI Research](https://mlsanity.com/paper/2609.22039/) (2026)

## Abstract (arXiv)

Programmers frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by LLMs. To mitigate these challenges, we introduce a novel workflow into computational notebooks that augments LLM-based code generation with an additional ephemeral UI step, offering users UI scaffolds as an intermediate stage between user prompts and code generation. We present this workflow in BISCUIT, an extension for JupyterLab that provides users with ephemeral UIs generated by LLMs based on the context of their code and intentions, scaffolding users to understand, guide, and explore with LLM-generated code. Through a user study where 10 novices used BISCUIT for machine learning tutorials, we found that BISCUIT offers users representations of code to aid their understanding, reduces the complexity of prompt engineering, and creates a playground for users to explore different variables and iterate on their ideas.