Read or watch at the original provider. Each choice has a learning purpose, prerequisites and limits. Full courses and optional deeper branches are not added silently to a route’s core allocation.
Source publication date, provider update date and LecXie review date are separate. Undated does not mean new. Access and prices may change; follow the provider’s current conditions.
University of Helsinki · MinnaLearn
Elements of AI
What you will learn: Explore a broader introductory treatment of AI as an optional deeper route.
Why this is worth the time: A deeper alternative to the introductory explanations. It overlaps with the beginner foundation; choose it for depth rather than adding compulsory reading.
Before this
Beginner; check provider requirements
Study time
Optional Chapter 1: suggested 30-minute exploration. Provider guidance: 4–8 hours per part, not 30 minutes for the course.
Access
Free course access with account; provider conditions apply
Currency / limits
Provider entry and FAQ checked 15 Oct 2026; full course completion not tested
Self-reported and shared across routes. Source date: not established · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
HM Treasury · Evaluation Task Force
Impact Evaluation of AI Interventions
What you will learn: Specify a baseline, comparison and outcome measures before starting a pilot.
Why this is worth the time: Connects the earlier notes to a testable pilot rather than a demonstration.
Before this
Task, safeguards, workflow and capability notes from stops 1–5
Study time
20 min selected sections; full guidance takes longer
Access
Free public guidance; no account needed
Currency / limits
Updated 15 May 2026; UK public-sector guidance, adapt to context
Self-reported and shared across routes. Source date: not established · Last update: 2026-05-15 · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
Anthropic · Rick Dakan & Joseph Feller
Lesson 2B: The 4D Framework
What you will learn: Use delegation, description, discernment and diligence to plan a human–AI task.
Why this is worth the time: A practical bridge from understanding a model to describing a useful task and taking responsibility for the result.
Before this
Basic model limits
Study time
Suggested 15-minute reflection; runtime not verified
Access
Free video; no Claude account to watch; full-course access not verified
Currency / limits
Published 12 Jun 2025; AI-fluency framework, not an independent outcome evaluation
Loads a third-party player only when selected. No autoplay. If the provider blocks playback, use the original link below.
Self-reported and shared across routes. Source date: 2025-06-12 · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
Harvard Business School AI Institute
Navigating the Jagged Technological Frontier
What you will learn: Ask which task and comparison a productivity claim actually tested.
Why this is worth the time: Introduces uneven task performance before you judge evidence for your own work.
Before this
Understand the basic idea of an AI model
Study time
15 min selected reading
Access
Free public research summary; no account needed
Currency / limits
21 Sep 2023; historical consultant study, not a 2026 benchmark
Self-reported and shared across routes. Source date: 2023-09-21 · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
IBM Technology
AI, Machine Learning, Deep Learning and Generative AI Explained
What you will learn: Distinguish the broad field of AI from machine learning, deep learning and generative systems.
Why this is worth the time: Introduces the vocabulary before models, prompts and evaluation.
Before this
None; introductory
Study time
Suggested 15-minute study activity; exact video runtime unverified
Access
Free public video; platform conditions apply
Currency / limits
Published 5 Aug 2024; introductory terminology, not current performance evidence
Loads a third-party player only when selected. No autoplay. If the provider blocks playback, use the original link below.
Self-reported and shared across routes. Source date: 2024-08-05 · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
Information Commissioner’s Office · UK
Guidance on AI and data protection
What you will learn: Identify data and accountability questions to take to the responsible specialist.
Why this is worth the time: Adds a UK data-protection lens to the general risk framework. Relevance depends on jurisdiction.
Before this
A candidate task and its information flows
Study time
15 min selected guidance
Access
Free public UK guidance; no account needed
Currency / limits
Under review after the Data (Use and Access) Act; UK-specific
Self-reported and shared across routes. Source date: not established · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
NIST
AI Risk Management Framework
What you will learn: Turn a vague risk concern into ownership, context, measurement and controls.
Why this is worth the time: Provides a general risk lens. It does not replace local controls or legal advice.
Before this
A candidate task and people affected by it
Study time
Suggested 25-minute exploration of Govern, Map, Measure and Manage; not a provider runtime
Access
Free public framework; no account needed
Currency / limits
AI RMF 1.0 (2023) is being revised; check current guidance before use
Self-reported and shared across routes. Source date: 2023-01-26 · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
Google
PAIR Guidebook: movie-recommendation walkthrough
What you will learn: Apply human-centred AI questions to a worked recommendation example.
Why this is worth the time: Optional noncoding worked example after system feedback/control. Text is enough; optional Colab is excluded from the core route.
Before this
Basic model and user-needs concepts
Study time
Suggested 30-minute walkthrough; not a verified runtime
Access
Public text; optional Colab needs an account
Currency / limits
Codelab entry checked 15 Oct 2026; provider may revise content
Self-reported and shared across routes. Source date: not established · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
Google PAIR
Feedback + Control
What you will learn: Plan what feedback users can give and what control they retain.
Why this is worth the time: Useful when a change practitioner moves from a model to an ongoing service. Feedback does not necessarily retrain a system immediately.
Before this
Basic AI concepts and a system boundary
Study time
Suggested 20-minute study activity
Access
Free public text; optional worksheet available at the original site
Currency / limits
Foundational guidebook originating in 2019; page undated
Self-reported and shared across routes. Source date: not established · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
Google PAIR
User Needs + Defining Success
What you will learn: Identify a user problem, desired outcome and whether AI adds value.
Why this is worth the time: Makes choosing a useful application more concrete; use it before choosing automation simply because it is possible.
Before this
A candidate task or user problem
Study time
Suggested 20-minute study activity
Access
Free public text; no account needed
Currency / limits
Foundational guidebook originating in 2019; page undated
Self-reported and shared across routes. Source date: not established · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
TensorFlow
TensorFlow Playground
What you will learn: Compare training and test loss as noise or the training split changes.
Why this is worth the time: Makes generalisation visible through a small neural-network example. It overlaps with our local training/test activity; choose one exercise, not both as a requirement.
Before this
Training/test distinction; optional deeper branch after foundations
Study time
Suggested 20-minute exploration; not a provider course duration
Access
Free browser simulation; no upload, camera, microphone or account needed
Currency / limits
Link and basic access reviewed 15 Oct 2026; simulation, not an LLM benchmark
Best viewed on a laptop for side-by-side controls. On mobile, use the local Learn from examples table and keyboard controls instead.
Self-reported and shared across routes. Source date: not established · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
3Blue1Brown · Grant Sanderson
Large Language Models explained briefly
What you will learn: Explain how language generation differs from knowing that a claim is true.
Why this is worth the time: A visual starting point for prediction and verification. Use the OpenLearn technical sections instead if you prefer text.
Before this
None; no coding needed
Study time
Suggested 15-minute study activity; video runtime not asserted
Access
Free public visual explainer; no account needed to read
Currency / limits
20 Nov 2024; mechanism primer, not a current model comparison
Loads a third-party player only when selected. No autoplay. If the provider blocks playback, use the original link below.
Self-reported and shared across routes. Source date: 2024-11-20 · Last update: not established · Checked 2026-10-15. Optional unless selected in a core stop; full courses remain separate.
Editorial maintenance
First editorial edition for review. Resource links and conditions have dated checks; maintenance is manual. No weekly automation or notification service is active.
A review should follow a source revision, broken link, access/cost change or a material new alternative. We compare relevance, evidence, learning value, accessibility and overlap before replacing a resource. Popularity is not the criterion.
New route versions retain a change record. Existing route records stay pinned; no automatic reshuffling or unreviewed replacement.
2026-10-15 · 1.0.0 · First AI and Workforce AI learning edition. Active route records pin their unit sequence and resource references; replacements require a new reviewed version.
