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Curriculum Designer

Create and curate learning materials

Enhances✓ Available Now

What You Do Today

Develop or source content — readings, videos, interactive activities, case studies, and simulations. Transform subject matter expert knowledge into engaging, accessible learning materials.

AI That Applies

Generative AI creates first-draft content, converts text to multiple formats (video scripts, interactive exercises, study guides), and adapts reading levels for different audiences.

Technologies

How It Works

The system tracks learner progress, competency assessments, and engagement patterns across the learning environment. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The output — first-draft content — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Content creation accelerates dramatically. First drafts of materials appear in minutes instead of days. You move from creator to curator and editor.

What Stays

Quality judgment — ensuring content is accurate, pedagogically sound, inclusive, and engaging — requires expertise. AI generates fast; you ensure it's good.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for create and curate learning materials, understand your current state.

Map your current process: Document how create and curate learning materials works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Quality judgment — ensuring content is accurate, pedagogically sound, inclusive, and engaging — requires expertise. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support generative AI tools tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long create and curate learning materials takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

What's the biggest bottleneck in create and curate learning materials today — and would AI address the bottleneck or just speed up something that's already fast enough?

They're prioritizing which operational processes to automate

your process improvement or lean lead

How much of create and curate learning materials follows repeatable rules vs. requires genuine judgment — and can we quantify that?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.