NXGen Learning Studio: the case in brief

This is the short version for investors and partners. The full story is at Why NXGen begins with a plan.

What’s changed

AI has made course content cheap to produce. Most instructional designers now use AI tools (ATD’s research), and authoring products compete on how fast they can generate outlines, pages and quizzes. In my experience, though, speed was never the scarce part. Good design decisions are: who the learning is for, what people need to be able to do afterward, and what would prove they can. Early research on AI-generated course design keeps pointing to the gap I’m going after: alignment between objectives, activities and assessment that looks right but is thin underneath.

What I learned building with AI

I’ve spent close to 15 years in learning and enablement, across higher education, learning technology consulting, and enterprise and AI education. My doctoral research focused on how ready instructional designers are for AI. When I started building with AI myself, I got speed but lost continuity. Research, AI conversations and building all lived in separate windows, and the design only held together because I was holding it together.

So I set out to make good design repeatable. Working backward from a curriculum that had succeeded, I came up with 37 planning questions grounded in backward design and adult learning. Then I condensed them into eight open prompts, so the planning stays rigorous without eating up hours.

What I’ve built

NXGen Learning Studio is an AI-first authoring workspace where planning comes before building:

• Learning Design workspace: the blueprint, goals and objectives, and alignment in one place. You approve the plan before the AI builds from it.
• Alignment: shows how objectives connect to pages and assessment.
• Objective checks: Bloom’s revised taxonomy or Webb’s Depth of Knowledge levels on objectives and questions, with wording checks and a flag when a question sits below its objective.
• AI Assist and drafting: suggestions you review before using, and drafts you can edit.
• Agent-ready by design: every change goes through the same validated, versioned pipeline, whether a person makes it or an AI agent does.

Who it’s for

It’s for instructional designers and enablement teams who need rigor without slowing down. It’s also for faculty and academic programs that have to show how outcomes and assessment line up for course design and program review. And it’s for newer practitioners, who get the kind of structure experienced designers carry around in their heads.

Where things stand

NXGen is in active development, and I’ve been using it extensively for my own work. Objectives can now be classified with Bloom’s revised taxonomy or Webb’s Depth of Knowledge. The eight-prompt intake is the next thing we’re integrating. Testing with practitioners is starting now: invited reviewers are working in a development version and leaving feedback through Reviewer Mode. NXGen Learning Studio is the first piece of a broader learning ecosystem we’re building.

What I’ll measure

I’ll be looking at:

• the quality of design decisions
• redesigns avoided after a first draft
• time from request to an approved plan
• ultimately, what learners can do afterward

If any of this resonates, I’d love to talk more. Just let me know!