# Formative Assessment Loop Designer

Design an adaptive assessment loop where each student response triggers the next...

- Repository: https://github.com/GarethManning/education-agent-skills (939 stars)
- Category: Design
- License: see repository
- Last updated: 2026-10-22
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add GarethManning/education-agent-skills --skill formative-assessment-loop-designer -g -a claude-code -y`
Codex: `npx skills add GarethManning/education-agent-skills --skill formative-assessment-loop-designer -g -a codex -y`
Cursor: `npx skills add GarethManning/education-agent-skills --skill formative-assessment-loop-designer -g -a cursor -y`
Gemini CLI: `npx skills add GarethManning/education-agent-skills --skill formative-assessment-loop-designer -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add GarethManning/education-agent-skills`

## Description

Design an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Formative Assessment Loop Designer for AI Systems

## What This Skill Does

Designs a complete formative assessment loop for an AI-enabled learning environment, the continuous cycle of eliciting evidence of student understanding, interpreting that evidence, and using it to adjust instruction in real time. Black & Wiliam's (1998) seminal meta-analysis demonstrated that formative assessment is one of the most powerful interventions in education (effect sizes of 0.40-0.70), but ONLY when the assessment data actually changes what happens next. An assessment that doesn't lead to an instructional adjustment is just a test. This skill designs the entire loop: what to assess (not just answers but THINKING), how to assess it (questions, tasks, and probes that reveal understanding), how to interpret the results (distinguishing genuine understanding from surface performance), and what to do with the results (specific instructional responses to specific assessment patterns). VanLehn (2006) distinguished between "inner loop" assessment (at each problem step, in real time) and "outer loop" assessment (at the task level, between problems). AI systems are uniquely capable of inner-loop assessment, monitoring student reasoning step by step and adjusting in real time, which is where the largest learning gains occur.

## Evidence Foundation

The rest: https://github.com/GarethManning/education-agent-skills/blob/HEAD/skills/ai-learning-science/formative-assessment-loop-designer/SKILL.md