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.
Literacy perspective
Interaction Literacy
How request structure, exchange design, examples, clarification, and feedback shape behaviour.
What lecturers should notice
Use this as an experimental lens
Look at how the exchange is framed and revised: goals, context, constraints, examples, questions, feedback, and criteria.
Related units
5 units make this literacy visible
The role may be primary, secondary, supporting, integrated, or part of the attack surface; consult each unit’s metadata for the exact relationship.
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.
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.
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.