# Implementation Considerations for Automated AI Grading of Student Work

arXiv: https://arxiv.org/abs/2506.07955 · HTML: https://mlsanity.com/paper/2506.07955/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering](https://mlsanity.com/paper/2606.12422/) (2026)

## Abstract (arXiv)

This study explores the classroom implementation of an AI-powered grading platform in K-12 settings through a co-design pilot with 19 teachers. We combine platform usage logs, surveys, and qualitative interviews to examine how teachers use AI-generated rubrics and grading feedback. Findings reveal that while teachers valued the AI's rapid narrative feedback for formative purposes, they distrusted automated scoring and emphasized the need for human oversight. Students welcomed fast, revision-oriented feedback but remained skeptical of AI-only grading. We discuss implications for the design of trustworthy, teacher-centered AI assessment tools that enhance feedback while preserving pedagogical agency.