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AI Research
arXiv CS.AI·Jun 12·5 min read
Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering
TL;DR
K-12 education faces challenges in grading efficiency and consistency. A large language model (LLM) grader was developed using context and prompt engineering to evaluate student work against a rubric.
✦ Why It Matters
Engineers can leverage LLMs to enhance educational assessment tools while ensuring teacher involvement in grading processes.
Key Takeaways
- Explore using LLMs for grading to enhance efficiency.
- Utilize AI for narrative feedback rather than numerical scores.
- Combine AI tools with teacher judgment for better assessments.
- Focus on mathematics and science for optimal LLM performance.
How It Works
The LLM grader employs context and prompt engineering to align its scoring with established educational rubrics. By analyzing student submissions against these rubrics, the model generates scores and feedback that reflect human evaluators' assessments.