Practical Generative AI for Educators

Course Overview

This curriculum is designed to move educators from passive users of generative AI tools (like ChatGPT or Claude) to strategic implementers. It addresses the friction of differentiation workloads and assessment integrity by teaching foundational prompt engineering and workflow automation.

Learning Objectives

• Conceptual: Understand the underlying mechanics of Large Language Models (LLMs), identifying bias and hallucination risks.
• Practical: Apply Zero-shot, Few-shot, and Persona prompting frameworks to automate lesson planning and generate differentiated reading materials.
• Strategic: Redesign traditional assessments to be "AI-resilient" while leveraging AI tools for ethical grading assistance and rubric generation.

Module 1: Demystifying AI

Focuses on establishing trust and understanding the limitations of generative models before integrating them into the classroom.

1.1 How LLMs Actually Work

A non-technical breakdown of predictive text models vs. search engines. Discussing the "Stochastic Parrot" concept.

1.2 The Hallucination Problem

• Identifying Failures: Analyzing real-world examples where AI generated plausible but factually incorrect historical or scientific data.
• Mitigation Strategy: "Trust but Verify." Teaching the workflow of using AI for structure/ideation, but relying on primary sources for factual claims.

1.3 Data Privacy & Ethics

Guidelines on what data is safe to input into public models. Understanding FERPA implications and avoiding the input of PII (Personally Identifiable Information) or student records.

Module 2: AI as a Co-Teacher

Hands-on application of prompt architecture to reduce the workload of differentiating instruction for diverse learner profiles.

2.1 Prompt Engineering Frameworks

Moving beyond simple questions to structured commands.

• Zero-Shot vs. Few-Shot: Providing examples (few-shot) to guarantee output format and tone.
• Persona Prompting: Assigning a role. (e.g., "Act as an expert reading interventionist for 5th graders...")

Module 3: Assessment & Academic Integrity

Addressing the most common faculty concern: "How do I know if the student wrote this?"

3.1 Designing AI-Resilient Assessments

Shifting away from generalized essays toward process-oriented and hyper-localized assignments.

• In-class synthesis: Combining personal reflection with AI-generated drafts.
• Local Contexts: Prompting students to solve problems specific to their local community, which generalized models struggle to fabricate accurately.

3.2 AI for Grading Assistance

Using models to generate comprehensive rubrics and provide initial feedback on syntax and structure, allowing the educator to focus on evaluating higher-order thinking and argumentation.

Evaluation & Capstone Project

The "Workflow Replacement" Capstone

Instead of a traditional quiz, educators must identify one bottleneck in their weekly administrative or planning workflow (e.g., writing weekly parent newsletters, generating IEP goal templates, or differentiating vocabulary lists).

Deliverable:

• A documented "Mega-Prompt" designed by the educator.
• The "Before" workflow (time spent manually).
• The "After" workflow (the AI-generated output and time saved).