Plan a Friday
Open the complete schedule, experiment, challenge kickoff, and teacher setup for a specific unit.
Lecturer guide
Evidence-based learning
Responsible collaboration
CAS Human-AI Collaboration
Plan each Friday, trace the curriculum through different lenses, and connect nine evidence-based challenges to adaptive mastery.
The course shifts focus
FROM“What can AI do?”
TO“How do we design human-AI collaboration so that reliable, responsible outcomes emerge?”
Start with your task
Four ways into the curriculum
The same course guidance can be entered from the teaching task or conceptual lens that matters now.
Explore a literacy
Trace what explains and shapes behaviour across Interaction, Mechanism, Data, System, and Human-Context.
Follow the collaboration levels
See how practice develops from asking and instruction toward reflection and orchestration.
Find an AI fundamental
Locate the concepts, demonstrations, caveats, and questions that explain observed results.
Progression
Nine Fridays, one learning arc
Each unit adds a form of control while contributing evidence to the participant’s emerging personal theory of control.
Unit 1From Asking to Structuring: Designing AI InteractionUnit 2From Structuring to Alignment: Clarifying Before GeneratingUnit 3Why Models Behave Differently: Parameters, Variability, and ControlUnit 4From Opinion to Evidence: Grounding AI in Data and SourcesUnit 5From Answers to Processes: Designing Human-AI Co-ConstructionUnit 6Reflection Loops: Critique, Revision, and Better ReasoningUnit 7Leadership, Responsibility, and Legitimation in Human-AI SystemsUnit 8Designing Workflows: From Single Chat to Reliable AI SystemsUnit 9Security and Trust Boundaries in Human-AI Collaboration
From Asking to Structuring: Designing AI Interaction
This Friday develops the unit's collaboration move through the specified literacy lens and prepares participants for the associated evidence-based challenge.
From Structuring to Alignment: Clarifying Before Generating
This Friday develops the unit's collaboration move through the specified literacy lens and prepares participants for the associated evidence-based challenge.
Why Models Behave Differently: Parameters, Variability, and Control
Participants learn to stop treating model variability as inexplicable noise. They learn to hold a task stable, vary one generation condition, repeat trials, and use the resulting evidence to decide when variability is useful, when it is risky, and which controls are appropriate.
From Opinion to Evidence: Grounding AI in Data and Sources
Participants learn that fluent output is only as defensible as the information made available to the model. They learn to inspect source selection, retrieval, chunking, citation, and claim support rather than treating a grounded answer as reliable merely because it contains references.
From Answers to Processes: Designing Human-AI Co-Construction
Participants learn that a clear request can still produce weak work when execution is treated as one opaque generation step. They learn to design phases, roles, handoffs, intermediate artefacts, and checkpoints that make complex work inspectable and steerable.
Reflection Loops: Critique, Revision, and Better Reasoning
Participants learn that asking for improvement is not yet a reflection process. They learn to separate construction, judging, revision, and verification; define explicit conditions of satisfaction; and test whether critique produces evidence-backed improvement rather than confident churn.
Leadership, Responsibility, and Legitimation in Human-AI Systems
Participants learn that a plausible AI recommendation does not become a legitimate decision merely because a human approves it. They learn to design and test the decision rights, evidence requirements, escalation routes, and opportunities for contestation that make human responsibility substantive rather than symbolic.
Designing Workflows: From Single Chat to Reliable AI Systems
Participants learn to move beyond a sequence of chat turns and design a controllable agentic system with explicit state, capabilities, routing, validation, permissions, stop conditions, and human approval. They learn when orchestration improves reliability and when it only adds complexity.
Security and Trust Boundaries in Human-AI Collaboration
This Friday makes security concrete as a control problem. Participants experience how LLM systems can confuse untrusted content with trusted instruction, and how workflow design, source governance, permissions, and human checkpoints affect controllability.
Module 2
From accumulated evidence to adaptive mastery
Participants select and interpret evidence from the nine challenges, redesign a context-specific work practice, stress-test its adaptability, and articulate a Human-AI Collaboration Blueprint and personal theory of control.
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