# AI-Generated Traces for Novice Programmers: Learning Effects and Learner Differences in a Multi-Institutional Study

arXiv: https://arxiv.org/abs/2606.03288 · HTML: https://mlsanity.com/paper/2606.03288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 49 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Builds on (most cited first, 2 of 2)

- ["Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students using Large Language Models](https://mlsanity.com/paper/2403.09409/) (2024)
- [ANVIL: Analogies and Videos for Lecturers](https://mlsanity.com/paper/2605.16295/) (2026)

## Abstract (arXiv)

Introductory programming (CS1) courses often struggle to support students' understanding of program execution. While visualizations can make execution processes explicit, their effectiveness depends on design and context, and empirical evidence for AI-generated visualizations remains limited. We propose Generated Animated Traces (GATs), AI-generated, analogy-based, narrated animations that coordinate source code, execution state, and conceptual analogies. We conduct a study at two institutions in CS1 courses (Python, N=961; Java N=151) comparing GATs to textual explanations. We measure immediate learning performance and experience, end-of-course engagement and exam performance. Results show that GATs can yield selective benefits for immediate learning, but benefits are context-dependent and short-term. We observe that GATs' influence on performance is moderated by learner engagement profiles. This finding underscores the importance of personalized approaches.