AIDAR: AI-Generated Code Detection via AST Structures

Large language models can generate convincing solutions to programming problems, making surface cues such as comments, naming, and style increasingly unreliable for identifying AI-generated code. Yet most existing approaches remain text-based and overlook the rich structural information in programs. We first introduce AST-TED, a non-neural baseline that compares complete abstract syntax trees (ASTs) using normalized tree edit distance. We then develop AIDAR by improving this framework in two ways: Discriminative Subtree Selection identifies informative local AST regions, while Prototype Distribution Matching replaces direct edit-distance comparison by matching distributions over shared structural prototypes for complete ASTs and selected subtrees. On CodeNet100 and a historical contest benchmark (C1), AIDAR achieves 95.05% and 99.00% accuracy, respectively, the highest among the evaluated methods. In a subsequent live contest (C2), we deployed AIDAR as part of a screening workflow that referred 17 of 1,176 full-score submissions for human review. The workflow's eight highest-ranked referrals matched the eight cases human experts flagged as suspected AI-generated code. These results demonstrate the value of AST structure for detection and for prioritizing human investigation. Code is available at https://anonymous.4open.science/r/AIDAR-3BCA for anonymous review.