AI-Generated code detection: an examination of current tools in education
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| Title: | AI-Generated code detection: an examination of current tools in education |
|---|---|
| Authors: | Cuellar Argotty, Juan Esteban |
| Contributors: | Manrique Piramanrique, Rubén Francisco, Facultad de Ingeniería |
| Publisher Information: | Universidad de los Andes Ingeniería de Sistemas y Computación Facultad de Ingeniería Departamento de Ingeniería de Sistemas y Computación |
| Publication Year: | 2025 |
| Collection: | Universidad de los Andes Colombia: Séneca |
| Subject Terms: | AI-generated code, AI-Generated Code Detection, Software Engineering Education, Ingeniería |
| Description: | This document explores the challenge of detecting AI-generated Python code in education, highlighting limitations of current detection tools, especially against simple obfuscation techniques. It emphasizes the need for advanced, resilient detection methods and ethical AI use in academic settings. ; This document explores the challenge of detecting AI-generated Python code within educational settings, focusing on first-semester student solutions on the Senecode platform. It outlines the creation of a dataset combining both human-written and AI-generated code (across multiple obfuscation variants) and evaluates seven widely used AI detectors. Despite each tool’s strengths in certain areas—such as high precision or high recall—none consistently excels, and simple code modifications substantially reduce detection accuracy. The study underscores the trade-off between minimizing false positives and maximizing true detection, highlighting the risk of unjustly penalizing students or overlooking AI misuse. Recommendations include developing more advanced, code-specific detection methods, employing a multi-layer approach that integrates human oversight, and fostering ethical AI use through clear academic policies. ; Pregrado |
| Document Type: | bachelor thesis |
| File Description: | 29 páginas; application/pdf |
| Language: | English |
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| Availability: | https://hdl.handle.net/1992/75503 |
| Rights: | Attribution-NonCommercial-NoDerivatives 4.0 International ; http://creativecommons.org/licenses/by-nc-nd/4.0/ ; info:eu-repo/semantics/embargoedAccess ; http://purl.org/coar/access_right/c_f1cf |
| Accession Number: | edsbas.95086CC5 |
| Database: | BASE |
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