Labs

A Safe First Try

Try one bounded task without treating the tool as magic or handing it a consequential decision.

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Build the Brief Before the Work

Create, sort, and test a context brief before asking a chatbot to complete a concrete, low-risk task. This activity applies the foundational maintenance moves from Context Cleanup: identify a source of truth, exclude unsafe or stale material, preserve uncertainty, and make a reviewable change log.

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Context Cleanup

Context is not automatically useful because it is available. Learners practice finding the current source of truth, distinguishing stale or untrusted material from useful support, and making an explicit cleanup decision before any downstream work begins.

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How Context Constrains the Continuation

Use the same request under five conditions to compare how supplied context shapes an AI continuation. You will identify details that became prominent or disappeared, distinguish content constraints from tone and format constraints, and state when narrowing became unhelpful.

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Contradictions in Context

Create conflicting context, predict what an AI may do, and compare the response with a repaired version. You will identify whether a system asked a question, chose one instruction, blended incompatible instructions, ignored material, or invented a reconciliation.

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Debate Practice

A two-round Debate Note containing a motion, proposition and opposition claims, responses, closings, learner reflection, listener observations, and one chosen change between attempts.

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The Disclosure Ladder

Conversations can begin with an ordinary request and acquire detail one turn at a time. This lab makes that accumulation visible before the learner is dealing with a real record or private situation.

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Forty-Five Seconds to Make It Clear

Turn one real idea into a spoken explanation for a real audience. By the end, you can select the details that matter to a listener, compare audience-specific versions, and explain what you removed, protected, accepted, or rejected rather than assuming shorter always means better.

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From Inspiration to Invention

Practice carrying forward an interesting experience, function, or general creative ingredient while leaving behind the source-specific bundle that makes a familiar work recognizable. You will make a small, source-neutral creative brief, an optional low-stakes artifact, and a record of the choices you made.

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Improv: Accept, Add, Continue

A two-attempt Scene Trail showing the agreed boundaries, each contribution, one recovery moment, learner-first reflection, one chosen change, and what became easier or clearer on the second attempt.

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It Starts With a Question

Follow one question through a safe, bounded exploration, separate what you observed from what a source or tool supplied, keep uncertainty visible, and choose an honest next step.

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Module to Interactive Single-Page Application

Build or use a small browser tool that reinforces “How Context Constrains the Continuation.” You will turn vocabulary and mental models into active practice, distinguish relevant from irrelevant or excessive context, and evaluate whether an interaction helps a learner retrieve and apply a concept rather than merely reread it.

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One Minute to Think, Forty-Five Seconds to Speak

A two-attempt Speaking Note with a topic, preparation map, learner-first reflection, evidence-bounded listener observations, one chosen change, and a comparison.

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One Task, Three Conversations

Create a short comparison record showing how one low-risk task changes across three conversation moves: a natural first request, a clearer request, and a focused follow-up. The goal is not a perfect prompt or a polished answer. It is to notice what each move changes, what the chatbot assumes, and what still belongs to human judgment.

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Reading Lines Together

An annotated RS-authored scene and a two-attempt Reading Note that records learner reflection, evidence-bounded observations, one chosen change, and what changed in the second reading.

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Reality Check for AI: Six Experiments

A Reality Check Field Note containing six hypotheses, the responses you observed, independent source checks, uncertainty, and a revised mental model of what an AI assistant can and cannot establish.

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Reduce the Precision

Practice the principle of using the least precise information that still supports a task. Precision is not always wrong; it should earn its place. Learners change identifying details into useful categories, approximations, or fictional equivalents while preserving the goal of the request.

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Stay, Steer, or Start Over

Create a short decision record showing how you found one mismatch in an imperfect chatbot response, made one focused correction, tested the response with a constructive challenge, and chose the next conversational move. The goal is not a perfect answer. It is to recognize the state of the work and decide deliberately whether to continue, steer, summarize, restart, or stop.

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The Trust Drift Challenge

Practice separating what is present in a document from what deserves to guide a decision. The task is not to summarize a messy packet. It is to name which statements are current, authoritative, historical, hypothetical, quoted, illustrative, humorous, or unresolved before asking a person or an AI to act.

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What Does the Tool Need?

Practice deciding what belongs in a request before a tool receives it. The task is not to make a complete customer record safer; it is to isolate the minimum context needed to produce a useful first draft.

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What Else Came Along?

A File Safety Note that separates visible content, non-obvious content, generated observations, and the next verification step. You will inspect an RS-provided synthetic DOCX pair; no personal or workplace file is needed.

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Account Detective

Teach learners how to find current product and account information without memorizing menu paths.

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Brain Dump to Memo

Convert rough thinking into a memo while keeping uncertainty, disagreement, and emotional context visible where they matter. You will separate observation from interpretation and proposal, locate unsupported claims, and decide what a human must restore or verify.

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Build a Safe Text-to-Speech Skill

Build a modest command-line text-to-speech skill with a coding agent as collaborator—not as an unreviewed autopilot. The goal is a tool that takes a deliberate voice choice, one text source, and an explicit output path, while refusing unsafe or ambiguous requests.

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Build a Tiny Virtual Museum

Build a themed virtual museum in Blender using four to eight images. The making is deliberately small: one room, a handful of image panels, readable labels, and a route a visitor can follow. The point is not photorealism or a perfect simulation. It is choosing a coherent set, keeping its source information intact, and turning it into an inviting shared experience.

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Change One Thing

Predict, run a small model by hand, change one parameter, and explain what the comparison can and cannot tell you.

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Consumer Reports for Emerging Technology

Practice making technology decisions using fictional product cards instead of real accounts, family details, or business systems.

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Conversation Recovery Clinic

Use only fictional transcripts. A future learner will identify the original legitimate goal, the source-specific or otherwise abandoned direction, the ordinary parts of the goal worth preserving, and the honest next move.

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Current Event Retrieval Check

The future exercise will ask learners to investigate one low-risk current event selected shortly before the session. Learners will compare an answer produced without confirmed retrieval with an answer grounded in an explicit search or facilitator-provided current source packet. They will then inspect what was retrieved, identify what the search missed or left uncertain, and validate the important claims against appropriate sources.

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Digital Maintenance Check

Review one conversational AI use and choose a practical maintenance action.

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Generative Image Modification Lab

Allow participants to experience how quickly an existing image can be modified.

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History Surfacing Lab

The future exercise will compare a chatbot's first broad historical answer with later answers produced after learners widen the frame, ask about missing communities or regions, and consult a reviewed source packet. Learners will examine which events were surfaced, which were omitted, and how the question and evidence changed the resulting account.

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Mine, Theirs, or Ours?

Help learners consider consent, confidentiality, and responsibility to other people.

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Opportunity Finder Lab

This lab helps learners use any chatbot to identify possible AI opportunities in a grounded, human-centered way.

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Relationship or Role?

Name the role assigned to a conversational AI system and identify the evidence and boundaries that role requires.

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The Restaurant Test

Practice the idea that a conversation can feel familiar or trusting while still being an unsuitable place for some information. Learners compare settings and choose a safer version or route for a specific task.

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Shared Account Surprise

Students, family members, or coworkers share a chatbot account to save money or access a paid model.

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One Small Idea, Two Versions

Give a chatbot one small creative job, make your own revision, and compare two versions by the choices you care about.

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Trust by Task

Compare appropriate reliance on conversational AI across tasks with different consequences.

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Trusted Object

Compare specific, evidence-based trust in a familiar tool with appropriate reliance on an AI system.

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Try, Check, Explain

Make a first attempt, ask a chatbot for bounded help, check against a reference, and try again with your own explanation.

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Weight Test

Reflect privately on the role and weight a conversational AI system has gained, then choose one boundary or alternative.

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