Practical AI Applications for Instructional Designers

AI is reshaping how instructional designers create courses, assessments, and learning materials, and this program is designed to help L&D professionals move beyond basic tool use into purposeful, structured, and responsible practice. Across four sessions, explore a progressive arc from structured prompting through retrieval-augmented generation (RAG), agentic pipelines, and governance, building practical prototypes along the way using ChatGPT and Claude.

A recurring theme throughout is evaluation as a core skill: the ability to test whether AI systems are producing accurate, aligned, and pedagogically sound outputs is what separates responsible adoption from risky experimentation. Each session includes hands-on artifact building, failure diagnosis exercises, and systematic verification practice.

Note: This program teaches participants to build prototypes using consumer and pro-tier tools, including ChatGPT Projects and Claude Projects. 
 

Learners will have the opportunity to:
1. Describe how generative AI works, including common failure modes relevant to instructional design contexts
2. Write structured prompts to draft, revise, and format instructional materials including syllabi, rubrics, and assessments
3. Apply AI to support course development, learner communications, and L&D operations
4. Assess AI outputs for accuracy, alignment, bias, and pedagogical appropriateness
5. Apply privacy and data protection practices when using AI with learner and curriculum content
6. Build a grounded course support chatbot using RAG that answers syllabus and policy questions with citations and safe escalation behavior
7. Design an agentic pipeline that automates assignment intake and grading support, including tool schemas and candidate output templates
8. Establish guardrails, evaluation scorecards, and a responsible AI rollout plan for L&D contexts

Topics include:

Session 1: AI Foundations and Prompting for Instructional Designers
- What AI is and is not in L&D contexts; hallucinations and failure modes
- Safe use fundamentals: privacy, confidentiality, and learner data sensitivity
- Prompt structure for instructional design work: role, task, context, constraints, output format
- Assessing outputs for accuracy, bias, tone, and alignment with learning objectives
- Building an ID Content Studio: rubric builder, assignment builder, course syllabus creator, and learning objective generator

Session 2: RAG Grounding and Grounded Course Support
- The context spectrum: from no context through RAG to full tool use
- RAG concepts: what happens under the hood vs. what you configure in Claude Projects
- Building a course support chatbot that answers learner questions with citations and escalation behavior
- Corpus curation: preparing syllabi, policy documents, and course materials for grounding
- Creating an evaluation harness to measure accuracy, citation coverage, and safe abstention

Session 3: Agents, Orchestration, and the Autonomous Grader
- Agentic AI concepts: tools, orchestration, and multi-step reasoning
- Designing an automated assignment intake and grading support pipeline
- Tool schemas and output templates for consistent, auditable grading artifacts
- Diagnosing failure modes: rubric drift, hallucinated feedback, proxy signals
- Partial working demo and pipeline walkthrough

Session 4: Governance, Evaluation, and Responsible Rollout
- Evaluating AI systems over time: scorecards, drift detection, and verification checklists
- Privacy, FERPA considerations, and data handling for learner-facing AI tools
- Moving from prototype to production: what changes and who you need to partner with
- Establishing guardrails for responsible AI use in L&D
- Developing a defensible AI implementation plan including change management