{
  "schema_version": 1,
  "version": "1.0.0",
  "reviewed": "2026-10-15",
  "editorial_status": "First editorial edition for review. Resource links and conditions have dated checks; maintenance is manual. No weekly automation or notification service is active.",
  "history": [
    {
      "version": "1.0.0",
      "date": "2026-10-15",
      "change": "First AI and Workforce AI learning edition. Active route records pin their unit sequence and resource references; replacements require a new reviewed version."
    }
  ],
  "resources": {
    "openlearn": {
      "provider": "The Open University · OpenLearn",
      "title": "An introduction to artificial intelligence",
      "url": "https://www.open.edu/openlearn/education-development/ai-matters/content-section-overview",
      "outcome": "Put AI in a social and ethical context before choosing a workplace use.",
      "prereq": "None; introductory",
      "effort": "6h full course; 20 min selected reading here",
      "access": "Free reading without sign-in; account for quizzes, tracking and participation statement",
      "certificate": "Provider statement after full completion; selected reading earns none",
      "version": "Updated 4 Mar 2026; title and access checked",
      "why": "Complements the technical explainer with social consequences.",
      "group": "Foundations",
      "id": "openlearn",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "systems",
        "learning"
      ],
      "source_date": null,
      "updated_date": "2026-03-04",
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "visual": {
      "provider": "3Blue1Brown · Grant Sanderson",
      "title": "Large Language Models explained briefly",
      "url": "https://www.3blue1brown.com/lessons/mini-llm/",
      "outcome": "Explain how language generation differs from knowing that a claim is true.",
      "prereq": "None; no coding needed",
      "effort": "Suggested 15-minute study activity; video runtime not asserted",
      "access": "Free public visual explainer; no account needed to read",
      "certificate": "No certificate offered on this page",
      "version": "20 Nov 2024; mechanism primer, not a current model comparison",
      "why": "A visual starting point for prediction and verification. Use the OpenLearn technical sections instead if you prefer text.",
      "group": "Foundations",
      "id": "visual",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": {
        "provider": "youtube",
        "video_id": "LPZh9BOjkQs",
        "official_url": "https://www.youtube.com/watch?v=LPZh9BOjkQs",
        "mechanism": "Official YouTube privacy-enhanced player, click to load",
        "documentation": "https://support.google.com/youtube/answer/171780?hl=en"
      },
      "topics": [
        "systems",
        "data_technology_ai"
      ],
      "source_date": "2024-11-20",
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "hbs": {
      "provider": "Harvard Business School AI Institute",
      "title": "Navigating the Jagged Technological Frontier",
      "url": "https://aiinstitute.hbs.edu/navigating-the-jagged-technological-frontier/",
      "outcome": "Ask which task and comparison a productivity claim actually tested.",
      "prereq": "Understand the basic idea of an AI model",
      "effort": "15 min selected reading",
      "access": "Free public research summary; no account needed",
      "certificate": "Reading does not award a certificate",
      "version": "21 Sep 2023; historical consultant study, not a 2026 benchmark",
      "why": "Introduces uneven task performance before you judge evidence for your own work.",
      "group": "Evidence",
      "id": "hbs",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "assurance",
        "work_design"
      ],
      "source_date": "2023-09-21",
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "nist": {
      "provider": "NIST",
      "title": "AI Risk Management Framework",
      "url": "https://airc.nist.gov/airmf-resources/airmf/5-sec-core/",
      "outcome": "Turn a vague risk concern into ownership, context, measurement and controls.",
      "prereq": "A candidate task and people affected by it",
      "effort": "Suggested 25-minute exploration of Govern, Map, Measure and Manage; not a provider runtime",
      "access": "Free public framework; no account needed",
      "certificate": "No certificate for reading",
      "version": "AI RMF 1.0 (2023) is being revised; check current guidance before use",
      "why": "Provides a general risk lens. It does not replace local controls or legal advice.",
      "group": "Safeguards",
      "id": "nist",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "governance_value_assurance"
      ],
      "source_date": "2023-01-26",
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "ico": {
      "provider": "Information Commissioner’s Office · UK",
      "title": "Guidance on AI and data protection",
      "url": "https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/guidance-on-ai-and-data-protection/",
      "outcome": "Identify data and accountability questions to take to the responsible specialist.",
      "prereq": "A candidate task and its information flows",
      "effort": "15 min selected guidance",
      "access": "Free public UK guidance; no account needed",
      "certificate": "No certificate for reading",
      "version": "Under review after the Data (Use and Access) Act; UK-specific",
      "why": "Adds a UK data-protection lens to the general risk framework. Relevance depends on jurisdiction.",
      "group": "Safeguards",
      "id": "ico",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "governance_value_assurance",
        "lifecycle_services"
      ],
      "source_date": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "ilo": {
      "provider": "International Labour Organization",
      "title": "How might generative AI impact different occupations?",
      "url": "https://www.ilo.org/resource/article/how-might-generative-ai-impact-different-occupations",
      "outcome": "Separate task exposure from a prediction that an entire job will disappear.",
      "prereq": "Basic model limits and a safeguarded task idea",
      "effort": "15 min reading and chart inspection",
      "access": "Free public article; no account needed",
      "certificate": "No certificate for reading",
      "version": "20 May 2025; potential exposure estimates, not realised job losses",
      "why": "Supports a task-by-task redesign conversation rather than a whole-job assumption.",
      "group": "Work design",
      "id": "ilo",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "work_design",
        "workforce_planning",
        "allocation_mobility"
      ],
      "source_date": "2025-05-20",
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "oecd": {
      "provider": "OECD",
      "title": "Bridging the AI skills gap",
      "url": "https://www.oecd.org/en/publications/bridging-the-ai-skills-gap_66d0702e-en.html",
      "outcome": "Distinguish broad AI literacy from specialised skills when planning learning.",
      "prereq": "A redesigned task and the people expected to do it",
      "effort": "6-page brief; 10 min suggested reading",
      "access": "Public brief and PDF; no account needed to read",
      "certificate": "No certificate for reading",
      "version": "24 Apr 2025; policy context, not an organisation’s skills assessment",
      "why": "Makes the capability plan broader than training people to use a single tool.",
      "group": "Capability",
      "id": "oecd",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "skills_learning",
        "leadership_participation"
      ],
      "source_date": "2025-04-24",
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "evaluation": {
      "provider": "HM Treasury · Evaluation Task Force",
      "title": "Impact Evaluation of AI Interventions",
      "url": "https://www.gov.uk/government/publications/the-magenta-book/guidance-on-the-impact-evaluation-of-ai-interventions-html",
      "outcome": "Specify a baseline, comparison and outcome measures before starting a pilot.",
      "prereq": "Task, safeguards, workflow and capability notes from stops 1–5",
      "effort": "20 min selected sections; full guidance takes longer",
      "access": "Free public guidance; no account needed",
      "certificate": "No certificate for reading",
      "version": "Updated 15 May 2026; UK public-sector guidance, adapt to context",
      "why": "Connects the earlier notes to a testable pilot rather than a demonstration.",
      "group": "Evaluation",
      "id": "evaluation",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "governance_value_assurance"
      ],
      "source_date": null,
      "updated_date": "2026-05-15",
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "elements": {
      "provider": "University of Helsinki · MinnaLearn",
      "title": "Elements of AI",
      "url": "https://course.elementsofai.com/",
      "outcome": "Explore a broader introductory treatment of AI as an optional deeper route.",
      "prereq": "Beginner; check provider requirements",
      "effort": "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",
      "certificate": "Introduction to AI certificate is free under provider requirements; Building AI certificate is paid. LecXie awards neither.",
      "version": "Provider entry and FAQ checked 15 Oct 2026; full course completion not tested",
      "why": "A deeper alternative to the introductory explanations. It overlaps with the beginner foundation; choose it for depth rather than adding compulsory reading.",
      "group": "Foundations",
      "id": "elements",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "embed": null,
      "topics": [
        "systems",
        "learning"
      ],
      "source_date": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "ibm": {
      "id": "ibm",
      "provider": "IBM Technology",
      "title": "AI, Machine Learning, Deep Learning and Generative AI Explained",
      "url": "https://www.youtube.com/watch?v=qYNweeDHiyU",
      "outcome": "Distinguish the broad field of AI from machine learning, deep learning and generative systems.",
      "prereq": "None; introductory",
      "effort": "Suggested 15-minute study activity; exact video runtime unverified",
      "access": "Free public video; platform conditions apply",
      "certificate": "No certificate for watching this video",
      "version": "Published 5 Aug 2024; introductory terminology, not current performance evidence",
      "why": "Introduces the vocabulary before models, prompts and evaluation.",
      "group": "Foundations",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "topics": [
        "systems",
        "data_technology_ai"
      ],
      "embed": {
        "provider": "youtube",
        "video_id": "qYNweeDHiyU",
        "official_url": "https://www.youtube.com/watch?v=qYNweeDHiyU",
        "mechanism": "Official YouTube privacy-enhanced player, click to load",
        "documentation": "https://support.google.com/youtube/answer/171780?hl=en"
      },
      "source_date": "2024-08-05",
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "playground": {
      "id": "playground",
      "provider": "TensorFlow",
      "title": "TensorFlow Playground",
      "url": "https://playground.tensorflow.org/",
      "outcome": "Compare training and test loss as noise or the training split changes.",
      "prereq": "Training/test distinction; optional deeper branch after foundations",
      "effort": "Suggested 20-minute exploration; not a provider course duration",
      "access": "Free browser simulation; no upload, camera, microphone or account needed",
      "certificate": "No certificate",
      "version": "Link and basic access reviewed 15 Oct 2026; simulation, not an LLM benchmark",
      "why": "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.",
      "group": "Evaluation",
      "reviewed": "2026-10-15",
      "review_status": "reviewed",
      "topics": [
        "systems",
        "data_technology_ai",
        "governance_value_assurance"
      ],
      "embed": null,
      "device_note": "Best viewed on a laptop for side-by-side controls. On mobile, use the local Learn from examples table and keyboard controls instead.",
      "source_date": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "four-d": {
      "id": "four-d",
      "provider": "Anthropic · Rick Dakan & Joseph Feller",
      "title": "Lesson 2B: The 4D Framework",
      "url": "https://www.youtube.com/watch?v=W4Ua6XFfX9w",
      "outcome": "Use delegation, description, discernment and diligence to plan a human–AI task.",
      "why": "A practical bridge from understanding a model to describing a useful task and taking responsibility for the result.",
      "access": "Free video; no Claude account to watch; full-course access not verified",
      "version": "Published 12 Jun 2025; AI-fluency framework, not an independent outcome evaluation",
      "effort": "Suggested 15-minute reflection; runtime not verified",
      "prereq": "Basic model limits",
      "group": "Practice",
      "topics": [
        "systems",
        "leadership_participation"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": "2025-06-12",
      "review_status": "reviewed",
      "embed": {
        "provider": "youtube",
        "video_id": "W4Ua6XFfX9w",
        "official_url": "https://www.youtube.com/watch?v=W4Ua6XFfX9w",
        "mechanism": "Official click-to-load privacy-enhanced player",
        "documentation": "https://support.google.com/youtube/answer/171780?hl=en"
      },
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "pair-needs": {
      "id": "pair-needs",
      "provider": "Google PAIR",
      "title": "User Needs + Defining Success",
      "url": "https://pair.withgoogle.com/chapter/user-needs/",
      "outcome": "Identify a user problem, desired outcome and whether AI adds value.",
      "why": "Makes choosing a useful application more concrete; use it before choosing automation simply because it is possible.",
      "access": "Free public text; no account needed",
      "version": "Foundational guidebook originating in 2019; page undated",
      "effort": "Suggested 20-minute study activity",
      "prereq": "A candidate task or user problem",
      "group": "Work design",
      "topics": [
        "work_design",
        "data_technology_ai"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": null,
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "pair-feedback": {
      "id": "pair-feedback",
      "provider": "Google PAIR",
      "title": "Feedback + Control",
      "url": "https://pair.withgoogle.com/chapter/feedback-controls/",
      "outcome": "Plan what feedback users can give and what control they retain.",
      "why": "Useful when a change practitioner moves from a model to an ongoing service. Feedback does not necessarily retrain a system immediately.",
      "access": "Free public text; optional worksheet available at the original site",
      "version": "Foundational guidebook originating in 2019; page undated",
      "effort": "Suggested 20-minute study activity",
      "prereq": "Basic AI concepts and a system boundary",
      "group": "Practice",
      "topics": [
        "knowledge_collaboration",
        "leadership_participation"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": null,
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "ifow-design": {
      "id": "ifow-design",
      "provider": "IFOW · CIPD · University of Warwick",
      "title": "Good Work Design and AI Adoption",
      "url": "https://www.ifow.org/publications/good-work-design-ai-adoption",
      "outcome": "Use a qualitative case to ask how design choices affect the work people do.",
      "why": "Grounds a team workflow discussion in eight cross-sector cases. The cases illustrate choices and mechanisms, not universal causal effects.",
      "access": "Public report; no account needed to view the publication page",
      "version": "18 May 2026; eight qualitative cases",
      "effort": "Suggested 25 minutes for one case; 60 minutes to compare selected cases",
      "prereq": "A workflow and a question about work quality",
      "group": "Work design",
      "topics": [
        "work_design",
        "leadership_participation"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": "2026-05-18",
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "turing-stakeholders": {
      "id": "turing-stakeholders",
      "provider": "The Alan Turing Institute",
      "title": "Engaging stakeholders in AI",
      "url": "https://www.turing.ac.uk/courses/engaging-stakeholders-ai",
      "outcome": "Map affected groups and plan how they can influence design.",
      "why": "Adds a structured participation method to a workflow or change plan; paper can replace optional collaborative-board tools.",
      "access": "Free account and enrolment required; optional Miro or paper; no iframe permission assumed",
      "version": "Provider course page reviewed 15 Oct 2026",
      "effort": "Provider range 1–5 hours; suggested 45–60 minute initial mapping, not completion",
      "prereq": "A candidate AI use and affected groups",
      "group": "Participation",
      "topics": [
        "leadership_participation",
        "knowledge_collaboration"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": null,
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "pai-impact": {
      "id": "pai-impact",
      "provider": "Partnership on AI",
      "title": "Shared Prosperity: job impact assessment",
      "url": "https://partnershiponai.org/paper/shared-prosperity/",
      "outcome": "Organise questions about job effects and what affected workers can tell you.",
      "why": "Useful alongside a workflow redesign. PAI is an industry-participating nonprofit; consult people rather than guessing their experience.",
      "access": "Free web/PDF resource; no account needed",
      "version": "7 Jun 2023; assessment guidance, not evidence that a particular implementation works",
      "effort": "Suggested 30–45 minutes for a bounded assessment",
      "prereq": "A workflow and access to affected workers or clearly recorded gaps",
      "group": "Work design",
      "topics": [
        "work_design",
        "allocation_mobility",
        "leadership_participation"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": "2023-06-07",
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "ifow-governance": {
      "id": "ifow-governance",
      "provider": "Institute for the Future of Work",
      "title": "Toward Good Automation: what the law says",
      "url": "https://www.ifow.org/publications/toward-good-automation-what-the-law-says-a-toolkit-for-workers-and-employers",
      "outcome": "Explore Involve, Assess, Act and Monitor through a selected workplace archetype.",
      "why": "A structured prompt for governance discussions. Provider calls it an interactive toolkit download; it is not presented here as a verified browser game or legal compliance test.",
      "access": "Public toolkit download; jurisdiction-sensitive legal application",
      "version": "22 Sep 2026; recheck applicability with the responsible specialist",
      "effort": "Suggested 25–40 minutes for one archetype",
      "prereq": "Workplace context and relevant jurisdiction",
      "group": "Safeguards",
      "topics": [
        "lifecycle_services",
        "governance_value_assurance"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": "2026-09-22",
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "ethics": {
      "id": "ethics",
      "provider": "University of Helsinki",
      "title": "Ethics of AI",
      "url": "https://ethics-of-ai.mooc.fi/",
      "outcome": "Explore ethical arguments and practise explaining a judgement.",
      "why": "An optional deeper branch when a beginner wants more than a short safeguard exercise. Written reflection adds depth; peer review may take weeks.",
      "access": "Free public chapters and assignments; sign-in saves provider progress",
      "version": "Course pages checked 15 Oct 2026; no completion gate imposed here",
      "effort": "Suggested 30–45 minutes on a selected chapter, not a course runtime",
      "prereq": "No specialist background; willingness to reflect",
      "group": "Foundations",
      "topics": [
        "governance_value_assurance",
        "leadership_participation"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": null,
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "lapata": {
      "id": "lapata",
      "provider": "The Alan Turing Institute · Royal Institution · Mirella Lapata",
      "title": "What is generative AI?",
      "url": "https://www.turing.ac.uk/events/turing-lectures-what-generative-ai",
      "outcome": "Develop a broader explanation of generative AI and its limits.",
      "why": "Optional lecture depth after the short visual primer. Foundational rather than a current model benchmark; exact event-day discrepancy remains unasserted.",
      "access": "Free official video; provider platform conditions apply",
      "version": "Foundational 2023 lecture; exact day not asserted",
      "effort": "Suggested 60-minute learning block; video runtime not verified",
      "prereq": "Basic introductory concepts",
      "group": "Foundations",
      "topics": [
        "systems",
        "data_technology_ai"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": null,
      "review_status": "reviewed",
      "embed": {
        "provider": "youtube",
        "video_id": "_6R7Ym6Vy_I",
        "official_url": "https://www.youtube.com/watch?v=_6R7Ym6Vy_I",
        "mechanism": "Official click-to-load privacy-enhanced player",
        "documentation": "https://support.google.com/youtube/answer/171780?hl=en"
      },
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "fairness": {
      "id": "fairness",
      "provider": "The Alan Turing Institute",
      "title": "AI Fairness in Practice",
      "url": "https://www.turing.ac.uk/news/publications/ai-ethics-and-governance-practice-ai-fairness-practice",
      "outcome": "Use a brief or workbook to structure questions about fairness.",
      "why": "Optional specialist depth for change practitioners. Public-sector origins require adaptation to HR; this is not a compliance checklist.",
      "access": "Free public brief/workbook",
      "version": "2023; check current context before use",
      "effort": "Suggested 30 minutes for a brief or 60–90 minutes for a selected workshop",
      "prereq": "Evidence and governance basics",
      "group": "Safeguards",
      "topics": [
        "governance_value_assurance",
        "lifecycle_services"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": null,
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    },
    "pair-codelab": {
      "id": "pair-codelab",
      "provider": "Google",
      "title": "PAIR Guidebook: movie-recommendation walkthrough",
      "url": "https://codelabs.developers.google.com/codelabs/pair-guidebook",
      "outcome": "Apply human-centred AI questions to a worked recommendation example.",
      "why": "Optional noncoding worked example after system feedback/control. Text is enough; optional Colab is excluded from the core route.",
      "access": "Public text; optional Colab needs an account",
      "version": "Codelab entry checked 15 Oct 2026; provider may revise content",
      "effort": "Suggested 30-minute walkthrough; not a verified runtime",
      "prereq": "Basic model and user-needs concepts",
      "group": "Practice",
      "topics": [
        "work_design",
        "data_technology_ai"
      ],
      "certificate": "Reading or participation here awards no certificate.",
      "reviewed": "2026-10-15",
      "source_date": null,
      "review_status": "reviewed",
      "embed": null,
      "updated_date": null,
      "review_date": "2026-10-15",
      "essential_or_optional": "Essential only when selected in a core stop; otherwise an optional alternative or deeper branch."
    }
  },
  "units": {
    "ai-language": {
      "id": "ai-language",
      "title": "Name the kind of AI",
      "goal": "Use precise language about a system before choosing it.",
      "resources": [
        "ibm"
      ],
      "explanation": "A supplier calls a fixed rule that routes expense claims “generative AI”. What would establish that description?",
      "activity": {
        "kind": "choice",
        "question": "A supplier calls a fixed rule that routes expense claims “generative AI”. What would establish that description?",
        "options": [
          "The product uses a modern dashboard",
          "Evidence that it generates new content from learned patterns, not just a fixed rule",
          "The supplier calls it intelligent"
        ],
        "correct": 1,
        "feedback": "A marketing label does not establish the mechanism. Ask what the system takes in, how it produces outputs and which part is learned or generative."
      },
      "note_prompt": "Describe one system’s input, output and mechanism without using the word intelligent.",
      "topics": [
        "systems",
        "data_technology_ai"
      ]
    },
    "ai-models": {
      "id": "ai-models",
      "title": "Prediction and verification",
      "goal": "Separate fluent language from evidence that a statement is true.",
      "resources": [
        "visual"
      ],
      "explanation": "An assistant produces a plausible reference that you cannot locate. What is the useful next action?",
      "activity": {
        "kind": "claims",
        "question": "An assistant produces a plausible reference that you cannot locate. What is the useful next action?",
        "options": [
          "Treat the citation format as evidence",
          "Ask for the same answer more confidently",
          "Verify the claim in an originating source or mark it unverified"
        ],
        "correct": 2,
        "feedback": "A generated citation is another output, not independent evidence. Check a source you can inspect and narrow any unsupported claim."
      },
      "note_prompt": "Name a claim and the originating source you would use to check it.",
      "topics": [
        "systems",
        "assurance",
        "knowledge_collaboration"
      ]
    },
    "ai-evidence": {
      "id": "ai-evidence",
      "title": "Test on a separate example",
      "goal": "Understand why a system can fit examples without generalising.",
      "resources": [
        "hbs"
      ],
      "explanation": "A classifier matches every training example. Does that establish performance on unseen cases?",
      "activity": {
        "kind": "training",
        "question": "A classifier matches every training example. Does that establish performance on unseen cases?",
        "options": [
          "Yes, because training accuracy is perfect",
          "No; compare a held-out test set and inspect important failure types",
          "Only if the model has more layers"
        ],
        "correct": 1,
        "feedback": "Training fit and performance on unseen cases answer different questions. The local toy below illustrates that distinction; it does not measure any real AI system."
      },
      "note_prompt": "Record what changed in training versus test loss and one limitation of this toy.",
      "topics": [
        "data_technology_ai",
        "governance_value_assurance"
      ]
    },
    "ai-boundaries": {
      "id": "ai-boundaries",
      "title": "Choose safe information",
      "goal": "Recognise when to stop and seek approval rather than paste data into a tool.",
      "resources": [
        "nist",
        "ico"
      ],
      "explanation": "You are trying an unapproved public AI tool. Which input is suitable for an initial learning exercise?",
      "activity": {
        "kind": "choice",
        "question": "You are trying an unapproved public AI tool. Which input is suitable for an initial learning exercise?",
        "options": [
          "A customer complaint with names removed but a unique case history retained",
          "An invented scenario with no personal or confidential facts",
          "A staff performance review because it is only a trial"
        ],
        "correct": 1,
        "feedback": "Removing a name may not remove identifiability or confidentiality. Use invented material for this exercise and follow your organisation’s approval and data rules before real work."
      },
      "note_prompt": "List what must stay out of the tool and who can approve a real use.",
      "topics": [
        "governance_value_assurance",
        "data_technology_ai"
      ]
    },
    "ai-prompt": {
      "id": "ai-prompt",
      "title": "Write a brief a person can check",
      "goal": "Make an AI-assisted task specific enough to review.",
      "resources": [
        "four-d",
        "pair-needs"
      ],
      "explanation": "Turn “write something useful about AI” into a bounded brief. Which improvement gives a reviewer a useful test?",
      "activity": {
        "kind": "choice",
        "question": "Turn “write something useful about AI” into a bounded brief. Which improvement gives a reviewer a useful test?",
        "options": [
          "Add more adjectives about quality",
          "Specify audience, source material, output limits and checks",
          "Ask it to act as the world’s best expert"
        ],
        "correct": 1,
        "feedback": "A brief helps when it defines the task and how a person will check the result. It cannot make an unsupported model claim true."
      },
      "note_prompt": "Write a short brief with audience, permitted inputs, output and two checks.",
      "topics": [
        "knowledge_collaboration",
        "systems"
      ]
    },
    "ai-personal-pilot": {
      "id": "ai-personal-pilot",
      "title": "Plan one bounded experiment",
      "goal": "Use your earlier notes to propose a small, reversible test.",
      "resources": [
        "evaluation"
      ],
      "explanation": "A demonstration looked impressive. What would make the next trial informative?",
      "activity": {
        "kind": "pilot",
        "question": "A demonstration looked impressive. What would make the next trial informative?",
        "options": [
          "Use it for all work next week",
          "Decide the baseline, comparison, failure boundary and review decision before starting",
          "Measure only how much text it produces"
        ],
        "correct": 1,
        "feedback": "The trial should answer a decision question. Include checking time, output quality and effects on people, then define when to stop."
      },
      "note_prompt": "Propose one task, a baseline, comparison, responsible person and stop/review rule.",
      "topics": [
        "governance_value_assurance",
        "data_technology_ai"
      ]
    },
    "ai-acceptance": {
      "id": "ai-acceptance",
      "title": "Choose a useful application",
      "goal": "Define the user problem and a useful outcome before selecting a tool.",
      "resources": [
        "four-d",
        "pair-needs"
      ],
      "explanation": "Two hypothetical tools have different quality and checking costs. Set the minimum acceptable quality before comparing price.",
      "activity": {
        "kind": "cost",
        "question": "Two hypothetical tools have different quality and checking costs. Set the minimum acceptable quality before comparing price.",
        "options": [
          "Pick the tool with the fastest raw output",
          "Use a task-specific quality threshold and record excluded failure types",
          "Use a public benchmark as the only decision"
        ],
        "correct": 1,
        "feedback": "A low cost per output can hide rework. A threshold makes quality a condition of comparison rather than something silently traded away."
      },
      "note_prompt": "Set the task’s quality threshold, evaluation examples and unacceptable failure types.",
      "topics": [
        "governance_value_assurance",
        "data_technology_ai"
      ]
    },
    "ai-review": {
      "id": "ai-review",
      "title": "Budget human review",
      "goal": "Allocate review authority and capacity as part of the task.",
      "resources": [
        "nist"
      ],
      "explanation": "A team can generate 200 drafts a day but can carefully review only 40. What should change?",
      "activity": {
        "kind": "choice",
        "question": "A team can generate 200 drafts a day but can carefully review only 40. What should change?",
        "options": [
          "Remove review to match production",
          "Limit volume or redesign the workflow around review capacity",
          "Let the tool approve its own uncertain cases"
        ],
        "correct": 1,
        "feedback": "Generation throughput is not delivery capacity. Bound the queue, assign review responsibility and define escalation for unresolved cases."
      },
      "note_prompt": "Write a review protocol: who checks what, at what volume, and which cases escalate.",
      "topics": [
        "leadership_participation",
        "knowledge_collaboration"
      ]
    },
    "ai-monitor": {
      "id": "ai-monitor",
      "title": "Monitor outcomes and drift",
      "goal": "Plan ongoing checks for the actual system version and task mix.",
      "resources": [
        "evaluation"
      ],
      "explanation": "After an update, average quality looks stable but rare serious errors increase. What should the team inspect?",
      "activity": {
        "kind": "choice",
        "question": "After an update, average quality looks stable but rare serious errors increase. What should the team inspect?",
        "options": [
          "Only the average score",
          "Failure types, affected groups and version-specific results against the stop rule",
          "Whether colleagues like the new interface"
        ],
        "correct": 1,
        "feedback": "An average can conceal a consequential tail. Keep the version, sample composition and error severity visible, and act on the agreed boundary."
      },
      "note_prompt": "Define a monitoring sample, a consequential-error indicator and a pause trigger.",
      "topics": [
        "governance_value_assurance",
        "data_technology_ai"
      ]
    },
    "ai-incident": {
      "id": "ai-incident",
      "title": "Rehearse a safe stop",
      "goal": "Prepare a response that includes affected people and a restart decision.",
      "resources": [
        "nist"
      ],
      "explanation": "A tool sends an incorrect internal instruction to several teams. Which response is most complete?",
      "activity": {
        "kind": "choice",
        "question": "A tool sends an incorrect internal instruction to several teams. Which response is most complete?",
        "options": [
          "Switch off the tool and assume the harm is undone",
          "Pause the action, identify recipients and consequences, correct the instruction, and review conditions for restart",
          "Delete the log to avoid confusion"
        ],
        "correct": 1,
        "feedback": "Stopping further action and addressing earlier consequences are different tasks. Preserve evidence and give the accountable owner a restart decision."
      },
      "note_prompt": "Write a short response card: pause, owner, evidence, correction, affected people and restart condition.",
      "topics": [
        "governance_value_assurance",
        "leadership_participation"
      ]
    },
    "ai-system": {
      "id": "ai-system",
      "title": "Map the whole intervention",
      "goal": "Define the system and its human dependencies before changing a component.",
      "resources": [
        "pair-feedback",
        "nist"
      ],
      "explanation": "A retrieval assistant depends on documents, permissions, a model, reviewers and a business process. Which boundary belongs in its evaluation?",
      "activity": {
        "kind": "choice",
        "question": "A retrieval assistant depends on documents, permissions, a model, reviewers and a business process. Which boundary belongs in its evaluation?",
        "options": [
          "The model alone",
          "The end-to-end intervention including access, knowledge currency and human decisions",
          "Only the search screen"
        ],
        "correct": 1,
        "feedback": "The outcome depends on more than the model. Missing or stale knowledge, access rules and review practices can dominate what users experience."
      },
      "note_prompt": "Map information owners, system components, decision owners and downstream consequences.",
      "topics": [
        "knowledge_collaboration",
        "data_technology_ai"
      ]
    },
    "ai-portfolio": {
      "id": "ai-portfolio",
      "title": "Compare learning value across pilots",
      "goal": "Choose a small portfolio that can answer useful questions.",
      "resources": [
        "evaluation"
      ],
      "explanation": "Three sponsors propose similar pilots using the same task and data. What would improve the portfolio?",
      "activity": {
        "kind": "choice",
        "question": "Three sponsors propose similar pilots using the same task and data. What would improve the portfolio?",
        "options": [
          "Approve all three as independent evidence",
          "Check overlap, coordinate a shared comparison and choose where different contexts add learning",
          "Choose the largest team because it gives the biggest claim"
        ],
        "correct": 1,
        "feedback": "Duplicate demonstrations do not automatically provide independent learning. Make context differences and the decision each pilot informs explicit."
      },
      "note_prompt": "Compare two pilot ideas by risk, evidence gap, overlap and the decision each would inform.",
      "topics": [
        "governance_value_assurance",
        "workforce_planning"
      ]
    },
    "ai-transition": {
      "id": "ai-transition",
      "title": "Design a staged transition",
      "goal": "Keep a credible fallback and learning loop during introduction.",
      "resources": [
        "evaluation",
        "pair-feedback"
      ],
      "explanation": "A successful trial is proposed for immediate organisation-wide rollout. What should the transition plan add?",
      "activity": {
        "kind": "choice",
        "question": "A successful trial is proposed for immediate organisation-wide rollout. What should the transition plan add?",
        "options": [
          "A fixed adoption target alone",
          "Stages, participant feedback, support capacity, measures and a fallback",
          "Mandatory use for every task"
        ],
        "correct": 1,
        "feedback": "Scale changes the context. A staged transition lets the organisation notice failure and learn while retaining a workable alternative."
      },
      "note_prompt": "Define the first stage, affected groups, support owner, fallback and evidence needed to expand.",
      "topics": [
        "leadership_participation",
        "skills_learning"
      ]
    },
    "ai-version": {
      "id": "ai-version",
      "title": "Control updates and retirement",
      "goal": "Keep the evidence meaningful as models, sources and processes change.",
      "resources": [
        "nist"
      ],
      "explanation": "The vendor changes the model while a pilot is running. What should the change practitioner preserve?",
      "activity": {
        "kind": "choice",
        "question": "The vendor changes the model while a pilot is running. What should the change practitioner preserve?",
        "options": [
          "Only the latest result",
          "Version history, prior evidence and an explicit retest/migration decision",
          "The original claim without mentioning the update"
        ],
        "correct": 1,
        "feedback": "A silent substitution can make before/after comparisons hard to interpret. Preserve the evaluated version and record why retesting or retirement is needed."
      },
      "note_prompt": "Write an update and retirement rule: trigger, owner, retest, communication and retained evidence.",
      "topics": [
        "governance_value_assurance",
        "data_technology_ai"
      ]
    },
    "wf-understand": {
      "id": "wf-understand",
      "title": "Understand AI at work",
      "goal": "Explain a task’s input, proposed output and the human check.",
      "resources": [
        "visual",
        "openlearn"
      ],
      "explanation": "An assistant drafts an internal agenda. Who should establish whether its proposed decisions reflect the meeting?",
      "activity": {
        "kind": "use-cases",
        "question": "An assistant drafts an internal agenda. Who should establish whether its proposed decisions reflect the meeting?",
        "options": [
          "The person responsible for the meeting",
          "The model, because it generated the draft",
          "Nobody if the language is clear"
        ],
        "correct": 0,
        "feedback": "Plausible wording cannot establish what people agreed. Keep a person responsible for verifying the agenda before use."
      },
      "note_prompt": "Choose a low-risk task and describe what a person must still decide and check.",
      "topics": [
        "systems",
        "work_design"
      ]
    },
    "wf-evidence": {
      "id": "wf-evidence",
      "title": "Judge a workplace claim",
      "goal": "Separate study findings from expectations about your own team.",
      "resources": [
        "hbs"
      ],
      "explanation": "A 2023 consultant study is offered as proof that your team will save the same amount of time in 2026. What is missing?",
      "activity": {
        "kind": "choice",
        "question": "A 2023 consultant study is offered as proof that your team will save the same amount of time in 2026. What is missing?",
        "options": [
          "A local task, tool and comparison that can test transfer",
          "A stronger adjective about transformation",
          "A larger adoption target"
        ],
        "correct": 0,
        "feedback": "The historical study is useful for asking about task variation. It does not establish the effect of a different system in your setting."
      },
      "note_prompt": "Name the claim, the original evidence, limits to transfer and a local comparison.",
      "topics": [
        "assurance",
        "governance_value_assurance"
      ]
    },
    "wf-protect": {
      "id": "wf-protect",
      "title": "Protect people and information",
      "goal": "Define information boundaries, ownership and challenge routes.",
      "resources": [
        "nist",
        "ico"
      ],
      "explanation": "An AI suggestion affects access to a training opportunity. Which safeguard matters beyond a technical undo button?",
      "activity": {
        "kind": "choice",
        "question": "An AI suggestion affects access to a training opportunity. Which safeguard matters beyond a technical undo button?",
        "options": [
          "A way for the person to understand, challenge and remedy the decision",
          "Only a log that the system ran",
          "A faster batch process"
        ],
        "correct": 0,
        "feedback": "Restoring a record may not restore an opportunity. Assign authority, meaningful review and a way to address consequences for people."
      },
      "note_prompt": "Record allowed data, excluded decisions, accountable owner, escalation and remedy.",
      "topics": [
        "lifecycle_services",
        "leadership_participation",
        "governance_value_assurance"
      ]
    },
    "wf-redesign": {
      "id": "wf-redesign",
      "title": "Redesign a task",
      "goal": "Separate task assistance from whole-job assumptions.",
      "resources": [
        "ilo"
      ],
      "explanation": "An occupation has some AI-exposed tasks. What follows for work design?",
      "activity": {
        "kind": "workflow",
        "question": "An occupation has some AI-exposed tasks. What follows for work design?",
        "options": [
          "The entire role should disappear",
          "Map the tasks, human responsibilities and new checking work before deciding",
          "Every person in the occupation needs the same tool"
        ],
        "correct": 1,
        "feedback": "Exposure estimates describe potential, not a local redesign decision. Include the work of checking, coordinating and handling exceptions."
      },
      "note_prompt": "Map before/after steps, who decides, who checks and what needs worker input.",
      "topics": [
        "work_design",
        "allocation_mobility",
        "workforce_planning"
      ]
    },
    "wf-capability": {
      "id": "wf-capability",
      "title": "Build capability through practice",
      "goal": "Plan learning that transfers into the redesigned task.",
      "resources": [
        "oecd"
      ],
      "explanation": "A team completes a short course. What would show capability in the task?",
      "activity": {
        "kind": "choice",
        "question": "A team completes a short course. What would show capability in the task?",
        "options": [
          "Attendance alone",
          "Observed practice on realistic examples with feedback and a transfer check",
          "A count of tool logins"
        ],
        "correct": 1,
        "feedback": "Attendance records participation. Practical evidence comes from what a person can do under relevant conditions with appropriate support."
      },
      "note_prompt": "Specify one practice task, feedback source, proficiency evidence and protected learning time.",
      "topics": [
        "skills_learning",
        "knowledge_collaboration"
      ]
    },
    "wf-pilot": {
      "id": "wf-pilot",
      "title": "Run a measured pilot",
      "goal": "Bring task, safeguards, workflow and capability into a testable proposal.",
      "resources": [
        "evaluation"
      ],
      "explanation": "A pilot saves drafting time but doubles checking time. How should it be judged?",
      "activity": {
        "kind": "choice",
        "question": "A pilot saves drafting time but doubles checking time. How should it be judged?",
        "options": [
          "Report drafting time only",
          "Compare the whole workflow’s quality, time and people effects against the baseline",
          "Treat usage growth as proof of value"
        ],
        "correct": 1,
        "feedback": "Net value depends on the whole workflow. Decide the comparison and what would justify continuing, changing or stopping."
      },
      "note_prompt": "Assemble task, safeguards, capability, baseline, comparison, measures, owner and stop/review rule.",
      "topics": [
        "governance_value_assurance",
        "work_design"
      ]
    },
    "wf-handoffs": {
      "id": "wf-handoffs",
      "title": "Make the handoffs explicit",
      "goal": "Allocate ownership across people and AI-assisted steps.",
      "resources": [
        "ifow-design",
        "hbs"
      ],
      "explanation": "A draft moves from a manager to HR and then to an employee. Where should the decision owner be clear?",
      "activity": {
        "kind": "workflow",
        "question": "A draft moves from a manager to HR and then to an employee. Where should the decision owner be clear?",
        "options": [
          "Only at the first step",
          "At each consequential handoff and for the whole decision",
          "Only if something fails"
        ],
        "correct": 1,
        "feedback": "Dividing work can obscure responsibility. Make who decides, checks, communicates and addresses consequences explicit across the chain."
      },
      "note_prompt": "Write the team protocol: decision owner, checker, handoffs and escalation.",
      "topics": [
        "work_design",
        "knowledge_collaboration",
        "lifecycle_services"
      ]
    },
    "wf-fair-allocation": {
      "id": "wf-fair-allocation",
      "title": "Inspect who gets the opportunities",
      "goal": "Include worker preferences, constraints and access in allocation decisions.",
      "resources": [
        "pai-impact",
        "ico"
      ],
      "explanation": "A scheduler optimises throughput but repeatedly allocates unpopular shifts to the same group. What should the leader do?",
      "activity": {
        "kind": "choice",
        "question": "A scheduler optimises throughput but repeatedly allocates unpopular shifts to the same group. What should the leader do?",
        "options": [
          "Accept the result because the objective was met",
          "Review the distribution, relevant constraints and worker input before approving allocation",
          "Remove the affected group from reporting"
        ],
        "correct": 1,
        "feedback": "An operational objective can conceal unequal burdens. Inspect who gains and loses, involve affected people and check the relevant policy and legal context."
      },
      "note_prompt": "List worker constraints, preferences, burden measures and the route to challenge an allocation.",
      "topics": [
        "allocation_mobility",
        "leadership_participation",
        "governance_value_assurance"
      ]
    },
    "wf-coaching": {
      "id": "wf-coaching",
      "title": "Protect time to learn and review",
      "goal": "Build a practice and coaching routine around the changed work.",
      "resources": [
        "turing-stakeholders",
        "oecd"
      ],
      "explanation": "Experts now spend all their time correcting AI outputs while newcomers lose access to learning tasks. What is a useful response?",
      "activity": {
        "kind": "choice",
        "question": "Experts now spend all their time correcting AI outputs while newcomers lose access to learning tasks. What is a useful response?",
        "options": [
          "Count corrections as proof the system works",
          "Redesign workloads so experts can coach and newcomers can practise safely",
          "Give everyone the same e-learning module and move on"
        ],
        "correct": 1,
        "feedback": "Checking may consume the capability needed to develop others. Protect practice, feedback and expert capacity rather than treating learning as spare time."
      },
      "note_prompt": "Plan a team practice cycle, expert coaching time and a check of retained judgement.",
      "topics": [
        "skills_learning",
        "leadership_participation",
        "talent"
      ]
    },
    "wf-team-review": {
      "id": "wf-team-review",
      "title": "Review team outcomes, not adoption",
      "goal": "Create a decision agenda that includes work quality and people.",
      "resources": [
        "evaluation"
      ],
      "explanation": "Tool usage rises while staff report additional checking and less control. What belongs in the review?",
      "activity": {
        "kind": "choice",
        "question": "Tool usage rises while staff report additional checking and less control. What belongs in the review?",
        "options": [
          "Usage alone",
          "Quality, end-to-end time, workload, control and the views of affected staff",
          "Only the supplier’s release notes"
        ],
        "correct": 1,
        "feedback": "Usage is not the outcome. A team review should make conflicting evidence visible and decide what to change or stop."
      },
      "note_prompt": "Write the team review agenda, evidence to gather and one explicit decision threshold.",
      "topics": [
        "performance",
        "leadership_participation",
        "governance_value_assurance"
      ]
    },
    "wf-participation": {
      "id": "wf-participation",
      "title": "Map participation before the change",
      "goal": "Identify whose knowledge and experience can change the design.",
      "resources": [
        "turing-stakeholders",
        "pai-impact"
      ],
      "explanation": "A programme consults sponsors and vendors but not the people doing the work. What evidence is missing?",
      "activity": {
        "kind": "stakeholders",
        "question": "A programme consults sponsors and vendors but not the people doing the work. What evidence is missing?",
        "options": [
          "Nothing if the sponsor approves",
          "Task knowledge, practical constraints and effects on people doing or receiving the work",
          "Only a wider communications campaign"
        ],
        "correct": 1,
        "feedback": "Participation can reveal how work is really done and what a design would change. Communication after a decision is not the same as influence before it."
      },
      "note_prompt": "Map affected workers, service recipients, representatives, decision owners and how they can influence the design.",
      "topics": [
        "leadership_participation",
        "lifecycle_services"
      ]
    },
    "wf-dependencies": {
      "id": "wf-dependencies",
      "title": "Trace capability dependencies",
      "goal": "Plan the supporting conditions across the workforce system.",
      "resources": [
        "ilo",
        "oecd"
      ],
      "explanation": "A new matching tool is ready, but skills records are incomplete and managers cannot release people. What constrains its value?",
      "activity": {
        "kind": "choice",
        "question": "A new matching tool is ready, but skills records are incomplete and managers cannot release people. What constrains its value?",
        "options": [
          "The interface colour",
          "Data quality, usable capacity, decision rights and mobility practices",
          "A lack of launch publicity"
        ],
        "correct": 1,
        "feedback": "The tool depends on organisational conditions. A proposed maturity strand is a prompt to investigate those conditions, not a score inferred from a technology purchase."
      },
      "note_prompt": "Map dependencies across planning, allocation, learning, services and data; record unknowns.",
      "topics": [
        "workforce_planning",
        "allocation_mobility",
        "skills_learning",
        "data_technology_ai"
      ]
    },
    "wf-evaluation": {
      "id": "wf-evaluation",
      "title": "Design an evaluation with affected groups",
      "goal": "Learn whether outcomes differ across teams or groups.",
      "resources": [
        "evaluation",
        "ifow-governance"
      ],
      "explanation": "A programme-wide average improves, while a small team’s workload worsens. What should the evaluation preserve?",
      "activity": {
        "kind": "pilot-review",
        "question": "A programme-wide average improves, while a small team’s workload worsens. What should the evaluation preserve?",
        "options": [
          "The positive average only",
          "Group-level evidence, context and the mechanism that could explain the difference",
          "A claim that the small team is resisting change"
        ],
        "correct": 1,
        "feedback": "Variation may point to different tasks, implementation or access to support. Investigate the explanation before attributing outcomes to attitudes."
      },
      "note_prompt": "Define an appropriate comparison, subgroup questions, feedback method and safeguards for reporting.",
      "topics": [
        "governance_value_assurance",
        "leadership_participation"
      ]
    },
    "wf-renewal": {
      "id": "wf-renewal",
      "title": "Keep change reversible and knowledge current",
      "goal": "Define ongoing ownership, renewal and retirement.",
      "resources": [
        "nist",
        "ifow-governance"
      ],
      "explanation": "After rollout, the task changes and the original training examples become obsolete. What should the programme retain?",
      "activity": {
        "kind": "choice",
        "question": "After rollout, the task changes and the original training examples become obsolete. What should the programme retain?",
        "options": [
          "The launch material as the permanent answer",
          "A named owner, review triggers, updated practice and a retirement/fallback decision",
          "Only a record of attendance"
        ],
        "correct": 1,
        "feedback": "Capability needs renewal as work changes. Preserve the old rationale and evidence while deciding what to update, retest or retire."
      },
      "note_prompt": "Write a change stewardship brief: owner, worker input, review triggers, capability renewal and exit/fallback.",
      "topics": [
        "knowledge_collaboration",
        "skills_learning",
        "governance_value_assurance"
      ]
    }
  },
  "areas": {
    "ai": {
      "title": "AI",
      "brand": "AyEye",
      "description": "Understand models, evidence and the systems around them. Choose a route from a first explanation to team practice or organisational change.",
      "concepts": [
        {
          "id": "models",
          "title": "Models & limits",
          "status": "established",
          "claim": "A model’s output needs checks appropriate to the task.",
          "boundary": "Mechanism knowledge does not establish the truth of a particular answer.",
          "resources": [
            "ibm",
            "visual"
          ],
          "topics": [
            "systems",
            "data_technology_ai"
          ]
        },
        {
          "id": "evidence",
          "title": "Evidence & evaluation",
          "status": "established",
          "claim": "Training fit and performance on unseen cases are different questions.",
          "boundary": "A past study or toy simulation is not a benchmark of today’s systems.",
          "resources": [
            "hbs"
          ],
          "topics": [
            "assurance",
            "governance_value_assurance"
          ]
        },
        {
          "id": "information",
          "title": "Information & access",
          "status": "emerging",
          "claim": "Knowledge currency and permissions shape an AI-assisted service.",
          "boundary": "Local data and access controls still need review.",
          "resources": [
            "nist",
            "ico"
          ],
          "topics": [
            "knowledge_collaboration",
            "data_technology_ai"
          ]
        },
        {
          "id": "human-work",
          "title": "Human judgement",
          "status": "established",
          "claim": "Checking and decision ownership belong in the workflow.",
          "boundary": "Human review is useful only when it has authority, time and relevant capability.",
          "resources": [
            "nist"
          ],
          "topics": [
            "leadership_participation",
            "work_design"
          ]
        },
        {
          "id": "change",
          "title": "Systems & change",
          "status": "emerging",
          "claim": "A staged introduction can test more than a model demonstration.",
          "boundary": "Transfer across teams and versions remains a question to investigate.",
          "resources": [
            "evaluation"
          ],
          "topics": [
            "workforce_planning",
            "skills_learning"
          ]
        },
        {
          "id": "value",
          "title": "Value & consequences",
          "status": "uncertain",
          "claim": "Net value in your setting requires a meaningful comparison.",
          "boundary": "Usage and output volume alone do not establish benefit.",
          "resources": [
            "evaluation"
          ],
          "topics": [
            "governance_value_assurance"
          ]
        }
      ],
      "routes": [
        {
          "id": "beginner",
          "version": "1.0.0",
          "title": "AI foundations: understand, check, try",
          "role": "Beginner",
          "units": [
            "ai-language",
            "ai-models",
            "ai-evidence",
            "ai-boundaries",
            "ai-prompt",
            "ai-personal-pilot"
          ],
          "outcome": "A personal experiment plan with checks and boundaries.",
          "prerequisites": "No technical background required. Start at the beginning.",
          "depth": "Core route",
          "deeper": [
            {
              "resource": "playground",
              "after": "ai-evidence",
              "purpose": "Explore generalisation in a visual simulation. Alternative to the local training/test toy, not a compulsory extra."
            },
            {
              "resource": "elements",
              "after": "ai-personal-pilot",
              "purpose": "Optional broader course after the short route; check provider details before enrolling."
            }
          ],
          "reviewed": "2026-10-15",
          "depths": {
            "explore": {
              "label": "Explore",
              "purpose": "Work through the fictional examples and feedback. Short notes are enough.",
              "suggested_minutes": 55
            },
            "apply": {
              "label": "Apply",
              "purpose": "Extend the activity into a hypothetical or approved non-sensitive work example and assemble the route output.",
              "suggested_minutes": 90
            }
          },
          "depth_disclosure": "LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total."
        },
        {
          "id": "team-leader",
          "version": "1.0.0",
          "title": "Lead an AI-assisted team task",
          "role": "Team leader",
          "units": [
            "ai-acceptance",
            "ai-review",
            "ai-monitor",
            "ai-incident"
          ],
          "outcome": "An operational acceptance and response card for one team task.",
          "prerequisites": "Understand model limits and evidence basics, or complete AI foundations first.",
          "depth": "Core route",
          "deeper": [
            {
              "resource": "evaluation",
              "after": "ai-monitor",
              "purpose": "Read the fuller guidance to strengthen the team evaluation plan."
            }
          ],
          "reviewed": "2026-10-15",
          "depths": {
            "explore": {
              "label": "Explore",
              "purpose": "Work through the fictional examples and feedback. Short notes are enough.",
              "suggested_minutes": 45
            },
            "apply": {
              "label": "Apply",
              "purpose": "Extend the activity into a hypothetical or approved non-sensitive work example and assemble the route output.",
              "suggested_minutes": 120
            }
          },
          "depth_disclosure": "LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total."
        },
        {
          "id": "change-practitioner",
          "version": "1.0.0",
          "title": "Introduce and renew an AI intervention",
          "role": "Change practitioner",
          "units": [
            "ai-system",
            "ai-portfolio",
            "ai-transition",
            "ai-version"
          ],
          "outcome": "A staged change decision memo with update and retirement rules.",
          "prerequisites": "Experience of a workflow or change project; foundation concepts assumed.",
          "depth": "Core route",
          "deeper": [
            {
              "resource": "nist",
              "after": "ai-version",
              "purpose": "Explore the full risk framework before choosing organisation-specific controls."
            },
            {
              "resource": "playground",
              "after": "ai-system",
              "purpose": "Optional visual simulation after the local training/test activity; choose either exercise."
            },
            {
              "resource": "pair-codelab",
              "after": "ai-transition",
              "purpose": "Optional noncoding example. Colab is outside the core route."
            }
          ],
          "reviewed": "2026-10-15",
          "depths": {
            "explore": {
              "label": "Explore",
              "purpose": "Work through the fictional examples and feedback. Short notes are enough.",
              "suggested_minutes": 45
            },
            "apply": {
              "label": "Apply",
              "purpose": "Extend the activity into a hypothetical or approved non-sensitive work example and assemble the route output.",
              "suggested_minutes": 120
            }
          },
          "depth_disclosure": "LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total."
        }
      ]
    },
    "workforce": {
      "title": "Workforce AI",
      "brand": "AyEye Workforce",
      "description": "Connect AI to work design, people, capability and responsible change. Follow a route that fits the decisions you actually make.",
      "concepts": [
        {
          "id": "tasks",
          "title": "Tasks & work design",
          "status": "established",
          "claim": "Task exposure is not a forecast that an entire job will disappear.",
          "boundary": "Redesign depends on the real workflow and people affected.",
          "resources": [
            "ilo"
          ],
          "topics": [
            "work_design",
            "workforce_planning"
          ]
        },
        {
          "id": "evidence",
          "title": "Evidence & context",
          "status": "established",
          "claim": "Workplace claims need a relevant task, tool and comparison.",
          "boundary": "Historical findings need local testing before transfer.",
          "resources": [
            "hbs"
          ],
          "topics": [
            "assurance",
            "governance_value_assurance"
          ]
        },
        {
          "id": "participation",
          "title": "People & participation",
          "status": "emerging",
          "claim": "Affected people can reveal constraints and consequences that a sponsor misses.",
          "boundary": "Participation needs meaningful influence, not only communication.",
          "resources": [
            "oecd",
            "ico"
          ],
          "topics": [
            "leadership_participation",
            "lifecycle_services"
          ]
        },
        {
          "id": "allocation",
          "title": "Allocation & mobility",
          "status": "emerging",
          "claim": "Matching capability also involves preferences, constraints and usable capacity.",
          "boundary": "A tool purchase does not establish organisational maturity.",
          "resources": [
            "ilo"
          ],
          "topics": [
            "allocation_mobility",
            "workforce_planning"
          ]
        },
        {
          "id": "capability",
          "title": "Skills & learning",
          "status": "established",
          "claim": "Participation in learning and proficiency at work are different evidence.",
          "boundary": "Local needs and transfer need observation and feedback.",
          "resources": [
            "oecd"
          ],
          "topics": [
            "skills_learning",
            "knowledge_collaboration"
          ]
        },
        {
          "id": "assurance",
          "title": "Governance & value",
          "status": "uncertain",
          "claim": "A workforce pilot should test quality, time and effects on people.",
          "boundary": "A positive average can conceal unequal effects or hidden checking work.",
          "resources": [
            "evaluation",
            "nist"
          ],
          "topics": [
            "governance_value_assurance",
            "data_technology_ai"
          ]
        }
      ],
      "routes": [
        {
          "id": "beginner",
          "version": "1.0.0",
          "title": "Workforce AI foundations",
          "role": "Beginner",
          "units": [
            "wf-understand",
            "wf-evidence",
            "wf-protect",
            "wf-redesign",
            "wf-capability",
            "wf-pilot"
          ],
          "outcome": "A one-page proposal for a bounded workforce pilot.",
          "prerequisites": "No AI background required. Work from task to evidence to action.",
          "depth": "Core route",
          "deeper": [
            {
              "resource": "elements",
              "after": "wf-understand",
              "purpose": "Optional deeper AI foundation. It overlaps with the introductory resources; do not add it to the core allocation."
            },
            {
              "resource": "ethics",
              "after": "wf-protect",
              "purpose": "Optional selected ethical reflection after the short safe-use activity."
            },
            {
              "resource": "lapata",
              "after": "wf-understand",
              "purpose": "Optional longer video explanation; an alternative foundation, not extra core content."
            }
          ],
          "reviewed": "2026-10-15",
          "allocation_minutes": [
            45,
            35,
            50,
            40,
            35,
            65
          ],
          "depths": {
            "explore": {
              "label": "Explore",
              "purpose": "Work through the fictional examples and feedback. Short notes are enough.",
              "suggested_minutes": 45
            },
            "apply": {
              "label": "Apply",
              "purpose": "Extend the activity into a hypothetical or approved non-sensitive work example and assemble the route output.",
              "suggested_minutes": 270
            }
          },
          "depth_disclosure": "LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total."
        },
        {
          "id": "team-leader",
          "version": "1.0.0",
          "title": "Lead people through an AI-assisted workflow",
          "role": "Team leader",
          "units": [
            "wf-handoffs",
            "wf-fair-allocation",
            "wf-coaching",
            "wf-team-review"
          ],
          "outcome": "A team working agreement covering ownership, fairness, coaching and review.",
          "prerequisites": "A real or hypothetical team workflow; AI/evidence basics assumed.",
          "depth": "Core route",
          "deeper": [
            {
              "resource": "ico",
              "after": "wf-fair-allocation",
              "purpose": "Investigate applicable UK data questions with the responsible specialist; replace with local guidance elsewhere."
            }
          ],
          "reviewed": "2026-10-15",
          "depths": {
            "explore": {
              "label": "Explore",
              "purpose": "Work through the fictional examples and feedback. Short notes are enough.",
              "suggested_minutes": 45
            },
            "apply": {
              "label": "Apply",
              "purpose": "Extend the activity into a hypothetical or approved non-sensitive work example and assemble the route output.",
              "suggested_minutes": 120
            }
          },
          "depth_disclosure": "LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total."
        },
        {
          "id": "change-practitioner",
          "version": "1.0.0",
          "title": "Design a workforce change that can learn",
          "role": "Change practitioner",
          "units": [
            "wf-participation",
            "wf-dependencies",
            "wf-evaluation",
            "wf-renewal"
          ],
          "outcome": "A change stewardship brief with participation, dependencies, evaluation and renewal.",
          "prerequisites": "Experience of a change project; complete the beginner route if AI basics are unfamiliar.",
          "depth": "Core route",
          "deeper": [
            {
              "resource": "evaluation",
              "after": "wf-evaluation",
              "purpose": "Study the full evaluation approach and work with an evaluation specialist where proportionate."
            },
            {
              "resource": "fairness",
              "after": "wf-evaluation",
              "purpose": "Optional fairness workshop before applying the governance brief to recruitment or other consequential contexts."
            }
          ],
          "reviewed": "2026-10-15",
          "depths": {
            "explore": {
              "label": "Explore",
              "purpose": "Work through the fictional examples and feedback. Short notes are enough.",
              "suggested_minutes": 45
            },
            "apply": {
              "label": "Apply",
              "purpose": "Extend the activity into a hypothetical or approved non-sensitive work example and assemble the route output.",
              "suggested_minutes": 120
            }
          },
          "depth_disclosure": "LecXie suggested study allocations, not provider runtimes or verified learning time. Optional deeper work is outside the total."
        }
      ]
    }
  },
  "review_basis": "Resource research supplied by the existing coordinating review on 15 Oct 2026; local activity content is original and fictional. Source dates, updates and review dates are separate fields. Individual video playback/captions and browser flows still require candidate QA."
}