AI-supported tools are increasingly used to generate feedback, highlight patterns in student responses and outputs, and inform instructional and system-level decisions.
This page provides a set of resources for examining how student work is interpreted in these contexts—particularly where differences in expression may be interpreted in differences in understanding.
The AI Interpretation Risk Check
The AI Interpretation Risk Check is a one-page tool for examining how features of student responses are interpreted in AI-supported systems.
It supports educators and leaders in considering how expression and understanding are related, and where additional evidence may be needed.
This is not a comprehensive protocol, and it is intended as a high-level starting point for examining how expression and understanding are related in context.
This tool is part of a broader set of edblogcast resources focused on reasoning, language, evidence, and interpretation in learning systems.
Download the AI Interpretation Risk Check (PDF)
Additional resources and examples will be added here over time.
Working Together
I work with educators, instructional leaders, and organizations to examine how AI-supported tools are used in practice—including how student work is interpreted, how instructional responses are shaped, and how decisions are make in team and system contexts.
This includes supporting teams in developing shared approaches to examining evidence, interpretation, and instructional decision-making across different learner populations.
To connect, email team@edblogcast.com