Implementation Considerations for Automated AI Grading of Student Work

2025-06-09

AI Summary

This study addresses the trustworthy deployment of AI-based automated grading systems in K–12 education—specifically, how to enable efficient and interpretable AI-assisted assessment while preserving teacher pedagogical autonomy and student trust. Method: Drawing on a co-design pilot involving 19 teachers, we integrated educational data mining, behavioral log analysis, structured surveys, and in-depth interviews. Contribution/Results: Findings reveal broad teacher acceptance of AI-generated narrative formative feedback, yet systematic rejection of fully automated scoring. We propose a “teacher-centered, human-AI collaborative, feedback-enhancing (not replacing)” paradigm for trustworthy AI assessment, with clearly delineated human–AI responsibility boundaries. Empirical results demonstrate that AI-augmented feedback significantly increases students’ revision willingness and response speed, whereas fully automated scoring undermines teacher authority and erodes student trust. The study provides empirically grounded design and deployment guidelines for educational AI systems.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Intelligent Robots: Embodied AI

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Exploring AI grading platform implementation in K-12 classrooms
• Assessing teacher trust in AI-generated rubrics and feedback
• Balancing AI efficiency with human oversight in grading

Innovation

• AI-powered grading platform for K-12
• Combines logs, surveys, and interviews
• Teacher-centered AI with human oversight