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

2026-01-27

AI Summary

This work addresses the challenge instructors face in understanding how students use generative AI to assist with coding and whether such usage aligns with pedagogical objectives. To this end, we propose Editrail, a novel system that, for the first time, embeds a visual representation of students’ interaction histories with AI directly into instructors’ routine code monitoring workflows, enabling fine-grained tracking of AI-assisted behaviors. By integrating interactive visualizations, edit histories, and AI behavior analysis, Editrail accurately identifies patterns of AI use that deviate from instructional goals and intelligently determines which students require intervention and when. This approach significantly enhances the efficiency of instructional oversight and supports more personalized assessment in programming education.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Computer Vision: Motion & Tracking

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• AI usage
• programming education
• student-AI interaction
• pedagogical alignment
• instructor visibility

Innovation

• AI usage visualization
• student-AI interaction
• programming education
• instructor intervention
• code history tracking