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Digital Learning Practice

Sample 8-week journey: “Is it safe to put this in?”

Illustrative journey, not a contract template

Behaviour (break-room): You want to paste something into an AI tool and you are not sure you should.

Observable outcome: Before pasting, the person can classify the content (safe / needs stripping / must not go in), say why, and choose an alternative when the answer is no.

Audience example: Housing officers, caseworkers, or office staff with access to a chatbot or Copilot-style tool.

Entry: Team has done the free lesson path and/or AI at work, six weeks / One day, one team.

Horizon: Still able to decide correctly 60 days after the last taught session.

WeekPhaseWhat happensLearner actionManager action
0DiscoveryAgree behaviour, rubric, privacy, baseline, delayed-check dateSponsor signs measurement plan
1Convert + baselineMap objectives from existing teaching; baseline scenarios (no teaching in the test)10–15 min baseline judgementsConfirm real examples from the team’s work
2LaunchShort refresh of the decision rules; first retrieval set with feedbackClassify 4–5 realistic pastes; explain one from memoryObserve one live decision that week if it occurs
3Spaced practiceGap; revisit; mix “tenant data”, “colleague email”, “public web text” (interleaving)Answer before feedback; one summarise-from-memory card after a gapRubric: observed / not / N/A
4Spaced practiceHarder variants; strip-and-retry patterns; when to use a local draft insteadShort scenarios + one “what would you remove?” taskCoaching cue on the most common miss
5Spaced practiceTransfer tilt: new tools / new document types not in week 2New scenarios onlySpot-check two people
6Light touchFewer items; critical objectives only; errors get a different explanation, not the same quiz again3–5 minTeam heatmap (patterns), not individual answer dumps
7Delayed window opensUnannounced-style delayed check (agreed in week 0)Retention + new transfer scenarioFinal observation pass
8ReadoutPlain report: retention, transfer, observations, guardrails, recommendationOptional feedback on burdenDecide stop / revise / scale
What we would report

Example shape, not promised numbers

  • Delayed correct classification rate vs baseline
  • Transfer accuracy on unseen document types
  • % of manager observations marked “observed safe decision”
  • Time cost per person per week
  • Opt-outs / complaints
Why this sample

Why not phishing for this sample? Phishing remains an excellent Pilot candidate (and the evidence page uses it to kill completion vanity). This sample matches the existing paid AI offers and the published free lesson “Is it safe to put this in?” so buyers see continuity.

Alternate sample (one-line): Same shape for “Tell a real message from a fake one” — practice must resemble the inbox decision; annual e-learning completion is not the endpoint.

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