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Classification of Systems-I01:26

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A dataset for human-written and AI-generated code source classification.

Ghizlane Boukili1, Said El Garouani1, Jamal Riffi1

  • 1LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez, 30003, Morocco.

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|February 18, 2026
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Summary
This summary is machine-generated.

A new dataset of 10,000 code samples aids in detecting AI-generated code. This resource helps computer science educators distinguish between human and artificial intelligence programming, improving academic integrity.

Keywords:
ChatGPTDetectionMachine learningProgramming languagesPrompt

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • AI code generation tools pose challenges to verifying student authenticity in computer science education.
  • Existing generic AI detection tools are insufficient for accurately identifying AI-generated code due to programming language specifics.

Purpose of the Study:

  • To introduce a specialized dataset for developing domain-specific AI code detection tools.
  • To address the gap in resources for researching AI-generated code detection.

Main Methods:

  • Creation of a dataset with 10,000 annotated code samples (5000 human-written, 5000 AI-generated).
  • Inclusion of samples across Python, Java, C, and C++.
  • AI-generated samples produced via ChatGPT API; human samples sourced from public repositories.
  • Each sample labeled by origin (human or AI) for model training.

Main Results:

  • The dataset enables robust training of machine learning and deep learning models for code source discrimination.
  • Facilitates the development of specialized tools for detecting AI-generated code.

Conclusions:

  • The specialized dataset is crucial for advancing research in AI-generated code detection.
  • Public availability of the dataset and experiment code supports further academic investigation and tool development.