Personalized Feedback & Progress Reports — AI for Student Assessment

Personalized Feedback: The Research on What Works

John Hattie's research identifies feedback as one of the most powerful influences on student learning (effect size 0.70), but only when it's the right KIND of feedback. Praise ("Good job!"), grades alone, or vague comments ("Needs improvement") have minimal impact.

Effective feedback has three components (Hattie & Timperley, 2007): 1. Feed Up — Where am I going? (Clarity on the goal/objective) 2. Feed Back — How am I doing? (Specific evidence of current performance) 3. Feed Forward — Where to next? (Actionable next steps)

Framework: Student Feedback Generator

Generate personalized feedback for the following student work:

Student: [Name or identifier]
Assignment: [Description]
Learning objective: [SWBAT...]
Rubric criteria: [List the criteria and student's score on each]

Student's work description:
[Describe what the student produced — key features, strengths, errors]

Generate feedback using the Feed Up / Feed Back / Feed Forward model:

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What you'll learn:

Generate specific, actionable student feedback that promotes growth rather than judgment
Use AI to write narrative progress reports that communicate learning clearly to families
Create feedback that references rubric criteria and provides clear next steps

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