Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering
The integration of large language models (LLMs) into educational assessment represents a transformative shift in classroom grading practices. While automated scoring systems and machine learning techniques have existed for decades, generative AI (GenAI) now enables educators to implement standards-based grading (SBG) with unprecedented efficiency and scale. This paper examines the theoretical foundations and evaluates an LLM grader that uses commercially available foundation models with context and prompt engineering to score student work against a rubric. Drawing on an empirical interrater agreement study using Massachusetts Comprehensive Assessment System (MCAS) data, we observed the Qu...
| Authors | Zewei Tian, Alex Liu, Lief Esbenshade et al. |
|---|---|
| Primary category | cs.CY |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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