Module 1

Nine Fridays, designed as evidence-producing learning cycles

Move from structured interaction toward security-aware orchestration while preserving the distinction between literacies, collaboration levels, and AI fundamentals.

Unit 01

From Asking to Structuring: Designing AI Interaction

Participants move from vague asking to structured request design. The Friday should make visible how goal, context, constraints, source expectations, and response form change the conditions under which an LLM predicts and generates.

Interaction literacyLevel 1: InstructionLLMs as next-token predictors; probabilistic output
Unit 02

From Structuring to Alignment: Clarifying Before Generating

Participants learn to improve the input context before production begins. The Friday should show how clarification surfaces assumptions, constraints, audience needs, and success criteria, thereby changing downstream output quality.

Interaction literacyHuman-Context literacyLevel 2: AlignmentContext as the condition for prediction; context limits
Unit 03

Why Models Behave Differently: Parameters, Variability, and Control

Participants make model behaviour experimentally observable by holding a task stable while varying generation conditions. The Friday should connect output differences to tokens, attention, sampling, temperature, repeated runs, and model variability.

Mechanism literacyLevel 1: Instruction with mechanistic controlTokens, tokenisation, attention, sampling, and variability
Unit 04

From Opinion to Evidence: Grounding AI in Data and Sources

Participants learn that reliable output depends on the informational basis available to the model. The Friday should make grounding, retrieval, chunking, source selection, and evidence inspection observable.

Data literacyLevel 1: Instruction with grounded evidence controlEmbeddings, vectorisation, retrieval, grounding, and bias
Unit 05

From Answers to Processes: Designing Human-AI Co-Construction

Participants shift from single-pass generation to designed co-construction. The Friday should show how phases, roles, perspectives, intermediate artefacts, and checkpoints create control surfaces.

System literacyInteraction literacyLevel 3: Co-constructionDecomposition, intermediate artefacts, roles, and process state
Unit 06

Reflection Loops: Critique, Revision, and Better Reasoning

Participants learn to use AI not only for production but for critique, evaluation, and revision. The Friday should separate construction, judging, and improvement through explicit criteria and artefacts.

Interaction literacyMechanism literacyLevel 4: ReflectionLLM-as-judge, conditions of satisfaction, and separate judging sessions
Unit 07

Leadership, Responsibility, and Legitimation in Human-AI Systems

Participants learn that plausible AI output does not automatically create legitimate or accountable decisions. The Friday should focus on decision rights, escalation, oversight, contestability, evidence requirements, and responsibility.

Human-Context literacyLevel 4: Reflection with responsible co-constructionFluent generation versus legitimate decision support
Unit 08

Designing Workflows: From Single Chat to Reliable AI Systems

Participants learn to design or tightly specify agentic workflows with state, tools, resources, prompts, permissions, validation, and control points. The Friday should connect orchestration to reliability, traceability, and accountability.

System literacyLevel 5: OrchestrationAgentic AI: planning, capabilities, workflow state, and control points
Unit 09

Security and Trust Boundaries in Human-AI Collaboration

Participants learn to treat LLM security as a control problem in human-AI collaboration. The Friday should make visible how untrusted text, retrieved sources, tool permissions, validation steps, approval checkpoints, and human responsibility shape whether a workflow remains controllable.

System literacyData literacyHuman-Context literacyInteraction literacyLevel 5: Orchestration with security-aware controlInstruction/data boundaries, prompt injection, permissions, and residual risk