# AI for Low-Code for AI

arXiv: https://arxiv.org/abs/2305.20015 · HTML: https://mlsanity.com/paper/2305.20015/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 31 May 2023.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [In-IDE Code Generation from Natural Language: Promise and Challenges](https://mlsanity.com/paper/2101.11149/) (2021)
- [Program Synthesis using Natural Language](https://mlsanity.com/paper/1509.00413/) (2015)
- [Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations](https://mlsanity.com/paper/1909.00031/) (2019)
- [Low-Code Programming Models](https://mlsanity.com/paper/2205.02282/) (2022)

## Built on by (most cited first, 3 of 3)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Empowering AI to Generate Better AI Code: Guided Generation of Deep Learning Projects with LLMs](https://mlsanity.com/paper/2504.15080/) (2025)
- [Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphs](https://mlsanity.com/paper/2504.11200/) (2025)

## Abstract (arXiv)

Low-code programming allows citizen developers to create programs with minimal coding effort, typically via visual (e.g. drag-and-drop) interfaces. In parallel, recent AI-powered tools such as Copilot and ChatGPT generate programs from natural language instructions. We argue that these modalities are complementary: tools like ChatGPT greatly reduce the need to memorize large APIs but still require their users to read (and modify) programs, whereas visual tools abstract away most or all programming but struggle to provide easy access to large APIs. At their intersection, we propose LowCoder, the first low-code tool for developing AI pipelines that supports both a visual programming interface (LowCoder_VP) and an AI-powered natural language interface (LowCoder_NL). We leverage this tool to provide some of the first insights into whether and how these two modalities help programmers by conducting a user study. We task 20 developers with varying levels of AI expertise with implementing four ML pipelines using LowCoder, replacing the LowCoder_NL component with a simple keyword search in half the tasks. Overall, we find that LowCoder is especially useful for (i) Discoverability: using LowCoder_NL, participants discovered new operators in 75% of the tasks, compared to just 32.5% and 27.5% using web search or scrolling through options respectively in the keyword-search condition, and (ii) Iterative Composition: 82.5% of tasks were successfully completed and many initial pipelines were further successfully improved. Qualitative analysis shows that AI helps users discover how to implement constructs when they know what to do, but still fails to support novices when they lack clarity on what they want to accomplish. Overall, our work highlights the benefits of combining the power of AI with low-code programming.