Working with AI in Instructional Design

AI as a Co-Pilot, Pedagogy in the Driver’s Seat

In modern instructional design, Artificial Intelligence is a powerful catalyst for efficiency, rapid prototyping, and creative exploration. However, AI is only as effective as the instructional designer guiding it.

I view generative AI tools not as a replacement for instructional rigor, but as a force multiplier that allows me to focus deeper on learner analysis, user experience, and performance outcomes.

Rapid Prototyping & Content Structuring: Using advanced language models to brainstorm initial task decompositions, draft rough-cut learning objectives, and outline preliminary content maps to accelerate the ADDIE/SAM development cycles.

Visual Asset Exploration & Prototyping: Leveraging AI image generation tools (such as Midjourney and DALL-E) during the early ideation phase to conceptualize branding, custom UI icons, and thematic design mockups before finalizing assets in Canva.

Scenario & Case Study Generation: Rapidly generating realistic branching-scenario dialogues, authentic workplace distractors, and multiple-choice distractor rationale tailored to specific audience profiles (e.g., athletics, corporate compliance).

Human-in-the-Loop Quality Control: Every piece of AI-assisted copy, framework, or graphic undergoes rigorous human review to ensure factual accuracy, instructional soundness, and alignment with established learning theories (e.g., ADDIE, Kirkpatrick evaluation models).

Data Privacy & Compliance: Strict adherence to data security standards, ensuring sensitive, proprietary, or personal information is never exposed to unsecure public models.

Transparency: Openly acknowledging where and how AI tools are utilized in my development pipeline—as seen across my project case studies (e.g., Athletic Concussion Protocol).