AI-Powered Differentiation: Prompts for the REACH Proficiency Scale

Abstract

This hands-on workshop demonstrates how educators can leverage Large Language Models (LLMs) to efficiently create differentiated instructional materials. We focus on a custom-developed prompt engineering framework designed to generate content specifically aligned with the Chicago Public Schools' REACH Performance Task rubric, with a special emphasis on scaffolding for English Language Learners (ELL) and diverse learners.

Learning Objectives

By the end of this seminar, participants were able to:

• Understand the core principles of effective prompt engineering for educational content.
• Apply the provided AI prompt framework to generate lesson plans, exit slips, and assessments.
• Modify AI-generated content to meet the specific proficiency levels of students as defined by the WIDA framework.
• Evaluate AI-generated materials for accuracy, bias, and pedagogical soundness.

Agenda

1. (15 min) Introduction: The Challenge of Differentiation
2. (20 min) Prompt Engineering 101: The Anatomy of a Powerful Prompt
3. (30 min) Hands-On Lab: Using the REACH-Aligned Prompt Framework
4. (15 min) Review & Refine: The Human-in-the-Loop Approach
5. (10 min) Q&A and Closing Remarks

Resources

Participants were provided with the following resources: