Design, prototype, and produce high-quality learning experiences for Eskwelabs. Fellows in this track help build new programs and improve existing ones by combining user empathy, pedagogy, and strong execution. This is a generalist role that overlaps with product/program management responsibilities—scoping learning “products,” coordinating specialists, and shipping materials—while applying specialized learning design skills.

AI is a core multiplier in this track: fellows use AI tools to rapidly get up to speed on unfamiliar subject areas Eskwelabs teaches, translate expert inputs into learnable structures, and produce polished deliverables faster.

Your work will contribute to the following outcomes:
1. New programs designed and validated with clear learning outcomes, audience fit, and delivery plans.
2. Stronger learning pathways that guide learners from onboarding → skills-building → portfolio outputs.
3. Higher-quality learning materials (slides, guides, activities, assessments) that are consistent, engaging, and instruction-ready.
4. More effective learner + instructor support through prompts and AI-enabled scaffolds (for teaching, feedback, and learner success).
5. Improved program performance via structured evaluation of instructor/mentor/learner/AI performance and clear recommendations that translate into backlog updates

A. New Program Research & Design
Research user needs, benchmark comparable programs, and design a new Eskwelabs learning product (or a major revision).

‍Example: Design a new short course: target learner profile, learning outcomes, syllabus outline, project brief, assessment strategy, and delivery plan.

B. Learning Pathways & Journey Design
Develop structured pathways across courses/modules/projects that create skill progression and momentum.

‍Example: Build a “Data → AI Builder” pathway with prerequisites, milestone projects, pacing guidance, and optional enrichment tracks.

C. AI Prompts & Support Systems for Learning
Create and test prompts (and prompt packs) that power learner/instructor support—feedback assistants, rubric helpers, lesson prep copilots, reflection coaches.

‍Example: A prompt system for “Instructor Support Copilot” that generates activity facilitation notes, anticipates learner misconceptions, and proposes timed check-ins.

D. Learning Materials Production (Core Build Work)
Produce or improve instructional materials for current and new programs: slides, cheatsheets, student guides, activity templates, assessments—sometimes with SMEs, sometimes without.

‍Example: Turn a rough module outline into an instructor-ready deck + student guide + 2 activities + rubric + quiz bank.

E. Evaluation, QA, and Performance Improvement Recommendations
Evaluate instructor, mentor, learner, and AI performance; assess overall program performance; translate findings into recommendations and a prioritized improvement backlog.

‍Example: Analyze post-module feedback + learner outputs, identify friction points, propose changes to sequencing, activities, rubric clarity, and mentor guidance.

This is a task-based, production-oriented learning design role. Work arrives as program needs (“design a new module”), execution needs (“produce materials for next cohort”), or performance questions (“what’s not working and how do we improve it?”). Fellows are expected to coordinate with specialists, make sound design decisions, and ship usable learning assets fast.

AI is used heavily for rapid topic familiarization, drafting, synthesis, activity ideation, rubric generation, and iteration. High performance requires strong judgment: verifying accuracy, aligning to outcomes, and ensuring the materials are teachable—not just well-written.

This track is ideal for people who want to build learning products end-to-end and enjoy the intersection of empathy + structure + execution.

You might come from backgrounds such as:
- Education, psychology, learning sciences, curriculum development
- Product/program management, operations, consulting (with strong writing + systems instincts)
- Communications, research, or community roles with facilitation interests
- Tech-curious generalists who like turning complexity into clear pathways

What matters most: you learn fast, coordinate well with specialists, and care deeply about learner outcomes.

- Learning outcome design and curriculum structuring
- Activity and assessment design (rubrics, checks for understanding, project scaffolds)
- Program/product thinking (scope, trade-offs, backlog, iteration)
- Research and synthesis (user needs, benchmarks, SME extraction)
- AI-enabled learning design (prompt packs, support copilots, evaluation prompts)
- Clear writing and information design for instructional materials
- Evaluating learning experience quality and proposing improvements

Traits
- User empathy: cares about learner clarity, motivation, and confidence
- Fast learner: comfortable designing for topics you didn’t start as an expert in
- Strong coordination: can work with SMEs, instructors, ops, and stakeholders
- Systems thinking: turns one-off materials into reusable patterns and templates
- Quality bar: insists on clarity, teachability, and assessment alignment

‍Skills
- Instructional design fundamentals (outcomes → activities → assessment alignment)
- Writing and editing for learning (clear, structured, learner-friendly)
- Basic evaluation literacy (feedback loops, rubric-based assessment, QA mindset)
- Prompt engineering for learning workflows (draft → critique → revise patterns)
- Project management basics: timelines, deliverables, stakeholder check-ins

- Learning Experience Designer / Instructional Designer

- Curriculum or Program Designer / Program Manager

- Learning Product Manager

- Education Operations / Learning Ops Lead

- L&D Specialist (corporate training)

- AI-Enabled Learning Design / Learning Tech (early-career path)

Tasks are listed in a shared tracker with context, target learners, required deliverables (e.g., pathway doc, module deck, assessment), deadlines, and submission format. Fellows claim tasks and deliver artifacts as links (Docs/Slides/Notion/Figma as relevant), plus any prompt packs and evaluation notes.

Fellows are expected to:
- Ask clarifying questions early (target user, outcomes, constraints, SME availability).
- Share a short “design plan” before building (outline, scope, assumptions).
- Submit deliverables with:
1. The artifact(s)
2. A short “how to use this” note (for instructors/ops)
3. Any assumptions, open questions, and recommended next stepsReviews are marked Approved or Revisions Needed, with clear, actionable feedback.

You will report to the Learning Experience Design Lead, who meets with fellows twice a month to review outputs, refine pedagogy patterns, and raise the quality bar on learning design, assessment alignment, and AI-enabled support systems.

You will be judged on whether your work makes Eskwelabs’ learning experiences clearer, stronger, and easier to deliver. Strong performance includes:
- Clear outcomes and alignment across content → activities → assessment
- Materials that instructors can actually use without extra translation
- Smart use of AI with accuracy checks and documented prompts
- Tight coordination with specialists and good versioning/documentation
- Recommendations that are practical and translate into a usable backlog