Page 254 - CyberSecurityEssentialsEnglish

6.3 Designing Lessons, Examples, and Learning Activities
◆ Key idea: the activity begins with the learning outcome.
AI is used in planning after the learning outcome and its evidence have been defined. Do
not begin with fifty attractive activities; begin with what the learner must be able to do,
then design an activity that lets them practice that very action.

AI can produce a lesson plan in seconds, but fast plans are often lists of generic activities not
tied to clear evidence of learning. They may propose a group discussion, a video, and a quiz

without stating what will change in the learner’s understanding, why the activity comes at
that point, or how the difficulty progresses. Planning therefore begins from the desired end,
working backwards.

The first step is to state an observable learning outcome. “Understand sustainability” is too

broad to guide design. But “compare the effect of two options using three environmental,
economic, and social criteria” defines an action and criteria. Next we define the evidence of
learning: a justified comparison table, a short presentation, a written decision, or a practical

performance. Then we choose the activity that lets the learner practice that same action.

The relationship among the outcome, the activity, and the assessment is called constructive
alignment. If the goal is for the learner to analyze, it is not enough for them to hear an
explanation and then answer a recall question. If the goal is to carry out a procedure, it is

not enough for them to write a definition of it. AI can inspect a plan and point out places of
misalignment, but it does not always know the realistic level of challenge, the class time, or

the learners’ experiences.

A lesson should include a moment that activates prior knowledge, then a focused
presentation, then an activity that requires production from the learner, then feedback, then
application or consolidation. There is no single formula for every lesson, but the presence of

these functions prevents the time from becoming a display of information. AI may support
each function differently: opening questions, an example, a scenario, or preliminary
feedback.

Active learning does not mean movement or a multiplicity of tools; it means that the learner

thinks with the material and uses it. Analyzing an error, predicting a result, comparing two
solutions, building an argument, or explaining a concept to a peer may be far more active
than a long digital game. The activity must be chosen according to the mental process

required, not the attractiveness of its form.

Case-based learning and simulation help transfer knowledge into a context close to practice.
A tool can play a hesitant client, a simulated patient, a manager asking for a justification, or

a historical figure within the limits of the sources. But the scenario must be controlled,

253