Output: A one-page proposal for a bounded workforce pilot.
Before this: No AI background required. Work from task to evidence to action.
Work through the fictional examples and feedback. Short notes are enough.
LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total.
0 / 6
Stops marked explored
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Stop 1 of 6
Understand AI at work
Explain a task’s input, proposed output and the human check.
Journey fit: Start with the prerequisite concepts above. The selected resource supplies context; the local case is where you use it.
Selected resource · Large Language Models explained briefly
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. Part of this stop’s selected reading.
Selected resource · An introduction to artificial intelligence
The Open University · OpenLearn
An introduction to artificial intelligence
What you will learn: Put AI in a social and ethical context before choosing a workplace use.
Why this is worth the time: Complements the technical explainer with social consequences.
Before this
None; introductory
Study time
6h full course; 20 min selected reading here
Access
Free reading without sign-in; account for quizzes, tracking and participation statement
Self-reported and shared across routes. Source date: not established · Last update: 2026-03-04 · Checked 2026-10-15. Part of this stop’s selected reading.
Original local activity · Fictional case
Explain a task’s input, proposed output and the human check.
An assistant drafts an internal agenda. Who should establish whether its proposed decisions reflect the meeting?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Choose a low-risk task and describe what a person must still decide and check.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 2 of 6
Judge a workplace claim
Separate study findings from expectations about your own team.
Journey fit: Build on Understand AI at work before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · Navigating the Jagged Technological Frontier
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. Part of this stop’s selected reading.
Original local activity · Fictional case
Separate study findings from expectations about your own team.
A 2023 consultant study is offered as proof that your team will save the same amount of time in 2026. What is missing?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Name the claim, the original evidence, limits to transfer and a local comparison.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 3 of 6
Protect people and information
Define information boundaries, ownership and challenge routes.
Journey fit: Build on Judge a workplace claim before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · AI Risk Management Framework
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. Part of this stop’s selected reading.
Selected resource · Guidance on AI and data protection
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. Part of this stop’s selected reading.
Original local activity · Fictional case
Define information boundaries, ownership and challenge routes.
An AI suggestion affects access to a training opportunity. Which safeguard matters beyond a technical undo button?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Record allowed data, excluded decisions, accountable owner, escalation and remedy.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 4 of 6
Redesign a task
Separate task assistance from whole-job assumptions.
Journey fit: Build on Protect people and information before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · How might generative AI impact different occupations?
International Labour Organization
How might generative AI impact different occupations?
What you will learn: Separate task exposure from a prediction that an entire job will disappear.
Why this is worth the time: Supports a task-by-task redesign conversation rather than a whole-job assumption.
Before this
Basic model limits and a safeguarded task idea
Study time
15 min reading and chart inspection
Access
Free public article; no account needed
Currency / limits
20 May 2025; potential exposure estimates, not realised job losses
Self-reported and shared across routes. Source date: 2025-05-20 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Original local activity · Fictional case
Separate task assistance from whole-job assumptions.
An occupation has some AI-exposed tasks. What follows for work design?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Map before/after steps, who decides, who checks and what needs worker input.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 5 of 6
Build capability through practice
Plan learning that transfers into the redesigned task.
Journey fit: Build on Redesign a task before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · Bridging the AI skills gap
OECD
Bridging the AI skills gap
What you will learn: Distinguish broad AI literacy from specialised skills when planning learning.
Why this is worth the time: Makes the capability plan broader than training people to use a single tool.
Before this
A redesigned task and the people expected to do it
Study time
6-page brief; 10 min suggested reading
Access
Public brief and PDF; no account needed to read
Currency / limits
24 Apr 2025; policy context, not an organisation’s skills assessment
Self-reported and shared across routes. Source date: 2025-04-24 · Last update: not established · Checked 2026-10-15. Part of this stop’s selected reading.
Original local activity · Fictional case
Plan learning that transfers into the redesigned task.
A team completes a short course. What would show capability in the task?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Specify one practice task, feedback source, proficiency evidence and protected learning time.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
Stop 6 of 6
Run a measured pilot
Bring task, safeguards, workflow and capability into a testable proposal.
Journey fit: Build on Build capability through practice before this activity. The selected resource supplies context; the local case is where you use it.
Selected resource · Impact Evaluation of AI Interventions
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. Part of this stop’s selected reading.
Original local activity · Fictional case
Bring task, safeguards, workflow and capability into a testable proposal.
A pilot saves drafting time but doubles checking time. How should it be judged?
Apply it to a working example
Use a hypothetical or approved non-sensitive example. Assemble task, safeguards, capability, baseline, comparison, measures, owner and stop/review rule.
A self-report of exploration, not verified proficiency. Your work is not sent to LecXie.
A one-page proposal for a bounded workforce pilot.
Assembled from your working notes. Empty sections remain prompts. This is a draft for discussion with the responsible people.
Optional deeper branches
Separate from the core route. Alternatives cover a similar outcome; deeper work extends it. Choose deliberately rather than repeating everything.
