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Module 6: Teaching and Leading AI-Augmented Teams

Learning objectives​

By the end of this module, you will be able to:

  • Structure a ten-minute explanation of a technical concept using the problem-before-solution pattern, and deliver it without slides.
  • Pick the right level of abstraction for an audience and adjust when you see you got it wrong.
  • Run a three-phase adoption plan that moves a team from individual experiments to a shared workflow with a rules file, review norms and an evaluation gate.
  • Choose metrics that measure delivery outcomes rather than tool usage, and explain why "acceptance rate" is the wrong number.
  • Recognise the three predictable objections to AI-assisted development and answer each one honestly.

Module content​

Chapter 1: Teaching a technical concept​

A repeatable structure for explaining anything technical in ten minutes: the problem, the naive approach and why it fails, the concept, one worked example, one thing that goes wrong. With a full worked example (explaining RAG to a mixed audience).

Chapter 2: Adoption and measurement​

Three phases - explore, standardise, scale - with the artefacts each one produces. What to measure (DORA metrics plus three AI-specific signals), what not to measure, and how to talk about the results.

Quiz​

Six questions.

Why a course on engineering ends with teaching​

Because in an AI-augmented team, the scarce skill is not writing code; it is transferring judgement. The engineer who can explain why the test comes before the generation, why the spec names non-goals, why the agent is read-only by default, multiplies the practice across a team. The one who can only do it multiplies nothing.