Teaching Generative AI in the Classroom
Why colleges run this
Departments are being asked to teach generative AI and to assess students who already use it. Both problems need answering together.
Course outcomes
CO1
Explain how a large language model produces output and where it fails
K2CO2
Construct prompts that produce reliable, structured output for teaching use
K3CO3
Design a lab session on generative AI for their own subject
K6CO4
Redesign an assignment so that it remains assessable when students use AI
K6CO5
Justify a departmental position on disclosure and academic integrity
K5Modules tap a module for its theory & lab
Hours shown are the recommended 5-day format — module time scales to the duration you pick.
01How the models work6 h
Theory
Tokens, context, training and inference, why models fabricate
Lab
Probe model behaviour on prepared prompts
Output
Annotated behaviour log
02Prompting as a teachable skill6 h
Theory
Role and constraint patterns, structured output, iteration
Lab
Build a prompt exercise for your own subject
Output
Subject-specific prompt exercise
03Building classroom material6 h
Theory
Lesson design, lab sheets, worked examples
Lab
Draft a two-hour lab for your own course
Output
Lab sheet ready to teach
04Assessment that survives AI6 h
Theory
Task design, process evidence, viva, in-class components
Lab
Redesign one assignment
Output
Redesigned assignment with rubric
05Academic integrity and policy6 h
Theory
Disclosure, detection limits, departmental policy
Lab
Draft a departmental position
Output
Draft policy note
Every participant receives
Certificate of completion Course material LMS access Interview question bank Mock interview & viva practice