I design learning experiences that make complex technology understandable.

And I build AI into how that work gets done.

AI Learning Experience Designer | Senior Instructional Designer | Technical Instructor · 15+ years in Technology · 7+ years in curriculum development

Most instructional designers approach technical content from the outside. I come at it from the inside. I spent over 15 years in network engineering, IT infrastructure, and systems management, and taught those same disciplines as a technical instructor before moving into instructional design. That combination shapes everything about how I work: how I design learning experiences, partner with subject-matter experts, and build hands-on labs and activities that reflect real-world practice. For the last seven years at Western Governors University, I've been doing exactly that across 60+ courses in the College of IT, working closely with assessment developers to ensure curriculum and evaluation are tightly aligned, with AI prompt engineering now at the core of my work.

Design Work

CASE STUDY

Integrating AI Prompt Engineering into Instructional Design at Scale

Western Governors University | College of IT

The Challenge

Designing technically rigorous online courses at scale requires balancing speed with quality. At WGU, course development involves coordinating with subject matter experts who bring deep technical knowledge but limited time: extracting that expertise, translating it into structured learning objectives, and building assessments that accurately reflect real-world practice is time-intensive work, often bottlenecked by back-and-forth revision cycles.

As the volume and complexity of course development projects grew across the College of IT, I began looking for ways to make the design process more efficient without sacrificing the instructional integrity that competency-based learning demands.

The Approach

What started as experimentation with AI tools on a single course project evolved into a structured, repeatable prompt-engineering workflow embedded throughout the full instructional design process.

I developed a library of purpose-built prompts targeting the specific friction points in the ID workflow:

Learning Objective Development: Rather than drafting objectives from scratch after SME interviews, I built prompts that could take raw SME input: notes, outlines, rough content drafts, and generate structured, Bloom 's-aligned learning objective candidates for review and refinement. This shifted the work from generation to evaluation, significantly compressing the early design phase.

Content Drafting and Refinement: AI-assisted drafting allowed me to produce structured first-draft content from SME source material, which SMEs could then review and correct rather than author from scratch. For technical subject matter experts whose bottleneck was writing time rather than knowledge, this fundamentally changed the collaboration dynamic.

Reusable Prompt Templates: As the workflow matured, I documented and systematized the most effective prompts into a reusable template library. This created a repeatable process that could be applied consistently across courses and, where applicable, shared with team members.

The Outcome

Applying this AI-integrated workflow across courses in the School of Technology produced measurable improvements across several dimensions:

Faster development cycles: Compressing the drafting and iteration phases reduced overall development time per course, allowing more projects to move through the pipeline without expanding resources.

More consistent content quality: Structured prompt outputs created a more uniform baseline for content and objectives across courses, reducing the variability that typically comes from juggling multiple projects and SMEs simultaneously.

Better alignment between objectives and assessments: Using AI to generate and cross-check alignment between learning objectives and assessment items caught gaps earlier in the process, reducing late-stage rework.

Reduced SME back-and-forth: Shifting SMEs from authors to reviewers, giving them AI-assisted drafts to react to rather than blank pages to fill, shortened review cycles, and improved SME engagement and satisfaction with the process.

Raw SME input

"Ok so basically what I want people to know is that when they're writing prompts for AI they need to think about what they're actually asking. Like a lot of people just type something vague and then complain the AI doesn't give them what they want. The main thing is you have to give it context. Also telling it who it is helps — like say it's a project manager or whatever role fits. And you need to say what format you want back otherwise it just gives you a wall of text. I've seen people ask it to 'write something about the project' and then wonder why it's not useful. Oh and the output — make sure you tell it if you want bullet points or an email or a table or whatever. I guess the other thing is don't be vague, like 'help me with this' is useless. You need a specific task. I think that covers the main stuff. Maybe also say something about how being specific saves time because you don't have to keep asking it to redo things."

AI-assisted learning objectives

By the end of this module, learners will be able to:

1

Explain why vague prompts produce inconsistent AI outputs by describing how specificity, context, and role assignment improve AI-generated results.

Source: "a lot of people just type something vague... you have to give it context"

2

Apply the Role + Task + Context + Output Format framework to construct a complete AI prompt for a workplace task.

Source: "telling it who it is helps... give it context... say what format you want"

3

Distinguish between weak and strong prompts by evaluating examples against the four-component framework.

Source: "'write something about the project'... 'help me with this' is useless"

4

Select an appropriate output format — such as bullet points, email, or table — based on the intended use of the AI-generated content.

Source: "tell it if you want bullet points or an email or a table"

5

Demonstrate how writing a specific, well-structured prompt reduces iteration time and improves the quality of AI outputs on the first attempt.

Source: "being specific saves time because you don't have to keep asking it to redo things"

Objectives follow Bloom's Taxonomy action verbs: explain, apply, distinguish, select, demonstrate.

Testimonials

"What makes James especially effective is that he brings a level of subject matter expertise to instructional design that is difficult to teach. His background in IT, systems, security, and infrastructure allows him to engage with highly technical content at a much deeper level than many instructional designers can."

“His ability to design engaging learning experiences while staying at the forefront of emerging technologies and AI integration is top-notch. Whether he is developing curriculum, exploring creative solutions with AI, or helping others understand complex concepts, James consistently brings both insight and practicality to the table.”

"I appreciated his willingness to share knowledge and help others navigate emerging technologies and AI thoughtfully rather than treating them as hype or shortcuts. James combines instructional design expertise, technical fluency, curiosity, and professionalism in a way that makes him a valuable asset to any learning or development team."

"James is a phenomenal instructional designer. His integration of AI and understanding of underlying principles consistently lead to outcomes that surpass the expectations for his role. He would be a valuable asset to any team as a leader or an individual contributor."

"James has shown me that he can deliver results and he is willing to put in the work to get those results. He holds himself to the same accountability that he expects from others. He can meet deadlines even when given shortened timelines and still turn in A quality work."

Credentials

Master of Education in Instructional Design — Western Governors University

Master of Science in Computer Information Systems — Boston University

Bachelor of Science in Business Administration — University of Phoenix

CompTIA Project+ · Project Management Certificate, WGU · Business Intelligence Certificate, WGU · Microsoft Certified Professional

About

My journey into learning design started in technology.  I spent more than 15 years working in network engineering, IT infrastructure, and systems management, and I also taught those subjects as a technical instructor before moving into instructional design full-time. That technical background still shapes how I approach my work today. I don’t just understand the content at a high level; I understand how it works in practice, which affects how I collaborate with subject matter experts, organize learning experiences, and decide what learners actually need to know to be successful.

My approach to learning design starts with identifying the gap between where learners are now and where they need to be, then figuring out the clearest and most effective way to close it. Over the past several years, I’ve incorporated AI prompt engineering into that process—not as a replacement for expertise, but as a way to speed up routine production work so I can spend more time on the strategic and creative decisions that still require human judgment. I view AI the same way I view any tool: useful when applied thoughtfully, and only valuable if it improves the quality of the work being produced.

Outside of work, I'm usually reading, building in my wood shop, or standing at the edge of Lake Michigan, reminding myself that not everything needs to be optimized.

Let’s Connect

Whether you're exploring a potential fit, have a project in mind, or just want to talk about the intersection of technology and learning — I'd welcome the conversation.