# Editrail: Understanding AI Usage by Visualizing Student-AI Interaction in Code

arXiv: https://arxiv.org/abs/2601.20085 · HTML: https://mlsanity.com/paper/2601.20085/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2026.

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

- [Stayin' Aligned Over Time: Towards Longitudinal Human-LLM Alignment via Contextual Reflection and Privacy-Preserving Behavioral Data](https://mlsanity.com/paper/2605.04029/) (2026)
- [TutorTrace: A Dataset and Taxonomy for Classifying Learner Behavioral States during AI-Assisted Programming Education](https://mlsanity.com/paper/2608.26184/) (2026)
- [VibeJam: A User Study Platform for Web Development with Agents](https://mlsanity.com/paper/2608.29889/) (2026)

## Abstract (arXiv)

Programming instructors have diverse philosophies about integrating generative AI into their classes. Some encourage students to use AI, while others restrict or forbid it. Regardless of their approach, all instructors benefit from understanding how their students actually use AI while writing code. Such insight helps instructors assess whether AI use aligns with their pedagogical goals, enables timely intervention when they find unproductive usage patterns, and establishes effective policies for AI use. However, our survey with programming instructors found that many instructors lack visibility into how students use AI in their code-writing processes. To address this challenge, we introduce Editrail, an interactive system that enables instructors to track students' AI usage, create personalized assessments, and provide timely interventions, all within the workflow of monitoring coding histories. We found that Editrail enables instructors to detect AI use that conflicts with pedagogical goals accurately and to determine when and which students require intervention.