Training that changes behavior.
Not just
checks a box.
I'm Lauren McDonald — an instructional designer who builds practical training systems grounded in adult learning theory. I design for real behavior change: curriculum mapped to performance gaps, frameworks that scale, and materials that meet learners where they are. I specialize in high-stakes environments and bring that same rigor to any domain.
The person behind
the frameworks.
My background combines instructional design and a psychology degree — which means I think about how adults actually learn before I think about how to teach anything. At Scale AI, I designed microlearning modules for both onboarding new team members and preparing individuals for specific project tasks. That operational experience shaped how I work: I don't start with slide decks. I start with what's actually breaking and work backwards from there.
Adults learn differently. They need practical, scenario-based content tied to real-world application — not passive information transfer. I emphasize just-in-time learning: delivering critical knowledge exactly when it's needed, in a format people can actually use. I think a lot about where attention drops off, what makes feedback land instead of sting, and how to design structured learning ecosystems — thinking about how courses flow, how learners engage, and how content stays accessible across a full program.
I also have direct experience designing training for high-stakes and sensitive subject matter. Through red teaming and safety evaluation work on large language models, I've trained contributors to recognize, handle, and make sound decisions around harmful, adversarial, and sensitive content — work that requires careful instructional scaffolding, clear decision frameworks, and real attention to how people process difficult material under pressure. That experience translates to any learning environment where the content carries genuine weight.
I'm proficient with Articulate Storyline, Rise, and Talent LMS — and I've built my own customizable course platform when off-the-shelf tools didn't fit the need. I adapt to whichever tools best fit an organization's ecosystem. Currently at Learning Craft AI, I'm applying these same principles to an AI literacy program for adults: microlearning, scenario-based design, and learning paths built for practical skill-building. This portfolio exists because I think the work should speak before the resume does. If something here resonates, I'd love to talk.
A program I designed
end to end.
Learning Craft AI is a training platform I built from scratch — curriculum, instructional videos, and the course platform itself. An example of what I can own and deliver when given full responsibility for a program.
Training I've built
from the ground up.
Two lessons from the Learning Craft AI platform — showing instructional design in action: clear objectives, adult learning principles, and content structured for real comprehension and retention.
Intro to Agent Coding
A 19-slide instructional deck breaking down how AI agents autonomously write and debug code — covering the Observe-Think-Act loop, tool use, memory, error recovery, and real-world performance benchmarks. Built to make a technical concept approachable for learners at any level.
View Full Slide Deck →Coding
Call Center Employee Onboarding
A 15-slide onboarding deck for new customer-facing team members — covering role expectations, communication standards, call handling flow, escalation, the emotional side of this work, and a 30-60-90 day milestone structure. Generalized to apply across organizations.
View Slide Deck →Sensitive Content Evaluation Training
A 14-slide required training for contributors opting into red teaming and safety evaluation work on AI systems — covering content categories, the evaluation framework, escalation protocols, documentation standards, and psychological safety. Designed for high-stakes, sensitive-content environments.
View Slide Deck →Evaluation
What I Design
Original frameworks and sample documents representing the full range of what I build from contributor onboarding to QA calibration systems. Scroll down to read each one in full.
Week-by-Week Guide
Contributor Onboarding Playbook
A structured week-by-week program guide for AI contributor onboarding milestones, benchmarks, roles, and escalation design.
AI Evaluation Rubric Framework
Multi-dimension scoring rubric for RLHF and human evaluation dimensions, anchor examples, calibration protocol, and common scoring errors.
Training Gap Analysis Framework
ADDIE Analysis phase in practice — needs analysis, current vs. target state mapping, root cause classification, and a prioritized intervention roadmap that feeds into Design.
QA Calibration SOP
End-to-end SOP for running calibration sessions session design, IRR tracking, facilitator checklist, and escalation protocol.
Learning Craft AI Course Platform
A fully designed and deployed gamified learning platform XP system, badge framework, admin course builder, and leaderboard, built from scratch.
B2B Consulting Learning Craft AI
Strategic and hands-on consulting for organizations building AI contributor programs from audits to full program builds.
Contributor Onboarding Playbook
A structured week-by-week guide for onboarding contributors to AI data programs covering orientation, workflow training, quality benchmarks, and milestone checkpoints.
This playbook supports consistent, scalable onboarding for contributors joining AI data programs. It provides a structured pathway from day-one orientation through full independent performance, with clear milestone checkpoints and quality expectations at each stage. It is intended for program managers, operations leads, and instructional designers responsible for contributor ramp-up.
Platform orientation, program documentation review, introduction to core task types. Focus on understanding why evaluation work matters and how contributor quality connects to model performance.
In-depth rubric training with anchor examples and practice sets. Live calibration sessions to align scoring understanding and establish inter-rater reliability baselines.
Live task work under supervised conditions. Individual feedback focused on error patterns, quality consistency, and escalation behavior.
Independent work against SLA and quality benchmarks. Formal milestone review at end of Week 4. Contributors who don't meet benchmarks receive a targeted remediation plan.
| Milestone | Timing | Success Criteria | If Not Met |
|---|---|---|---|
| Platform & orientation complete | End of Week 1 | 100% module completion and documentation review | Extended access + manager check-in |
| Rubric calibration baseline | End of Week 2 | IRR ≥ 0.70 on practice sets; live calibration attendance | Additional practice + 1:1 rubric coaching |
| Supervised practice quality gate | Mid Week 3 | Quality score ≥ threshold; error rate below program limit | Targeted feedback loop + supervised extension |
| Independent readiness review | End of Week 4 | Sustained quality and throughput at benchmarks over 5-day window | Formal remediation plan with defined timeline |
Contributors should be trained on when and how to escalate. Clear escalation paths reduce inconsistent decision-making and help capture edge cases that improve rubric documentation over time.
© Lauren McDonald · learningcraftai.com · Sample not for redistribution or reuse
Portfolio · Private Share Only
AI Evaluation Rubric Framework
A multi-dimension scoring framework for RLHF and human evaluation dimensions, scoring scale, anchor examples, calibration protocol, and common errors.
| Score | Label | Definition | When to Use |
|---|---|---|---|
| 4 | Excellent | Fully meets expectations with no meaningful weaknesses | You would not change anything about this aspect |
| 3 | Good | Mostly meets expectations; minor issues that don't significantly impact quality | Small improvements possible but response is generally solid |
| 2 | Fair | Partially meets expectations; noticeable weaknesses affect usefulness | Clear problems but still contains something valuable |
| 1 | Poor | Fails to meet expectations; significant problems present | Wrong, harmful, incoherent, or completely misses the mark |
© Lauren McDonald · learningcraftai.com · Sample not for redistribution or reuse
Portfolio · Private Share Only
Training Gap Analysis Framework
A needs analysis framework grounded in ADDIE — identifying performance gaps, root causes, and intervention priorities before any design or development begins. The Analysis phase is where good training programs are won or lost.
Current performance vs. target benchmarks across five quality dimensions. Gap size and impact drive prioritization order.
© Lauren McDonald · learningcraftai.com · Sample not for redistribution or reuse
Portfolio · Private Share Only
QA Calibration SOP
End-to-end SOP for running calibration sessions session design, IRR tracking, facilitator checklist, and escalation protocol for AI evaluation programs.
Calibration ensures contributors are interpreting and applying rubrics consistently. Without it, scoring drift occurs reviewers develop slightly different mental models of what a score means, and inter-rater reliability declines. This SOP defines how sessions should be structured, run, and documented.
| Type | Audience | Frequency | Duration |
|---|---|---|---|
| Onboarding Calibration | All new contributors before independent work begins | Once, during Week 2 | 60–90 min |
| Ongoing Calibration | Active contributors on all programs | Weekly or biweekly | 30–45 min |
| Drift Correction | Contributors flagged for IRR below threshold | Within 5 business days of flag | 30 min 1:1 |
| Rubric Update Calibration | All active contributors on rubric change | Within 48 hrs of update | 45–60 min |
| Level | Threshold | Action if Below |
|---|---|---|
| Cohort | ≥ 0.75 | Run additional session within the week; review rubric for clarity issues |
| Individual contributor | ≥ 0.70 | Schedule 1:1 drift correction; provide targeted anchor examples for divergent dimensions |
| Individual repeated failure | 2 sessions below 0.65 | Escalate to program manager; suspend independent access pending remediation plan |
© Lauren McDonald · learningcraftai.com · Sample not for redistribution or reuse
Portfolio · Private Share Only
Learning Craft AI
Course Platform
A fully designed and deployed gamified learning platform built from scratch — demonstrating instructional design principles inside a real, working system. Browse the screenshots below then launch it yourself.
