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Evaluating large language models for criterion-based grading from agreement to consistency.

Da-Wei Zhang1, Melissa Boey2, Yan Yu Tan2

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Summary
This summary is machine-generated.

Large language models (LLMs) can perform criterion-based grading effectively. Prompt engineering with specific criteria enhances LLM grading accuracy, showing domain knowledge is key for educational feedback.

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Natural Language Processing

Background:

  • Large language models (LLMs) show promise in various applications.
  • Automated grading systems are crucial for educational efficiency.
  • The efficacy of LLMs in criterion-based grading requires thorough evaluation.

Purpose of the Study:

  • To assess the capability of LLMs in criterion-based grading.
  • To investigate the effect of detailed prompt engineering on grading performance.
  • To understand the role of domain-specific knowledge in LLM grading.

Main Methods:

  • Quantitative analysis comparing LLM performance against human benchmarks.
  • Evaluation of LLMs using well-established grading criteria.
  • Experimentation with prompt engineering techniques to refine LLM instructions.

Main Results:

  • Free LLMs demonstrated proficiency in criterion-based grading.
  • LLMs exhibited a nuanced understanding of grading criteria.
  • Domain-specific understanding proved more critical than model complexity for accurate grading.

Conclusions:

  • LLMs are capable of delivering criterion-based educational feedback.
  • Prompt engineering significantly impacts LLM grading quality.
  • LLMs offer a scalable solution for providing educational feedback.