# Who's the Leader? Analyzing Novice Workflows in LLM-Assisted Debugging of Machine Learning Code

arXiv: https://arxiv.org/abs/2505.08063 · HTML: https://mlsanity.com/paper/2505.08063/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2025.

## Built on by (most cited first, 2 of 2)

- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [Investigating the Effects of LLM Use on Critical Thinking Under Time Constraints: Access Timing and Time Availability](https://mlsanity.com/paper/2603.08849/) (2026)

## Abstract (arXiv)

While LLMs are often touted as tools for democratizing specialized knowledge to beginners, their actual effectiveness for improving task performance and learning is still an open question. It is known that novices engage with LLMs differently from experts, with prior studies reporting meta-cognitive pitfalls that affect novices' ability to verify outputs and prompt effectively. We focus on a task domain, machine learning (ML), which embodies both high complexity and low verifiability to understand the impact of LLM assistance on novices. Provided a buggy ML script and open access to ChatGPT, we conduct a formative study with eight novice ML engineers to understand their reliance on, interactions with, and perceptions of the LLM. We find that user actions can be roughly categorized into leading the LLM and led-by the LLM, and further investigate how they affect reliance outcomes like over- and under-reliance. These results have implications on novices' cognitive engagement in LLM-assisted tasks and potential negative effects on downstream learning. Lastly, we pose potential augmentations to the novice-LLM interaction paradigm to promote cognitive engagement.