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Automated Grading of Linux/Bash Examinations Using Large Language Models

● high · 2026-07-03llm, automated-grading, cs-education, formative-assessment, higher-ed, scaffolding, learning-analytics, ai-literacy, k-12

Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira (2026)

This paper presents an LLM-based grading system for Linux/bash command-line examinations, applying a four-level cognitive taxonomy to assess student work in programming courses. The system addresses the scalability challenge of rising enrolments by providing Automated Grading that captures both partial correctness and conceptual understanding. The approach demonstrates high agreement with human graders, suggesting a viable path toward Formative Assessment at scale. Results indicate that LLMs can evaluate command-line proficiency more nuancedly than traditional rule-based autograders, offering detailed AI Feedback Quality feedback for students.

Connected Concepts

  • LLM
  • Automated Grading
  • Formative Assessment
  • AI Feedback Quality

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    • Citation

    Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira (2026). Automated Grading of Linux/Bash Examinations Using Large Language Models. arXiv:2607.02432.