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dangerous claims prevented, and the objective clarified: is it communication, diagnosis, or
decision-making?
Generating examples is a strong area for AI, but it requires deliberate variation. A single

example may lead the learner to tie the concept to surface features. It is better to offer
examples that differ in domain, language, and data while the underlying structure stays

constant. At the same time, cultural representation, names, occupations, and images must
be reviewed so that the content does not reproduce stereotypes or exclude particular
experiences.

A tool can be used to adapt an activity to different levels of support without changing the

learning outcome. Some learners may receive a glossary or an organizing table, while others
receive additional data or an open question. Differentiation does not mean automatically
lowering expectations; it means changing the route of access or the amount of scaffolding

while keeping an appropriate objective.

A good plan is executable. It must state the time, the resources, how roles are distributed,
what the teacher does during the activity, what happens if the technology fails, and how
quick evidence of understanding is gathered. AI may propose more than the time allows, so

the ideas must be turned into a precise scenario rather than a list of wishes.

FIGURE 6-4: ALIGNMENT BETWEEN THE LEARNING OUTCOME, THE ACTIVITY, AND THE ASSESSMENT

What to notice: The learner must practice in the activity the same action they will be asked
to demonstrate in the assessment, and the evidence of learning must reflect the level of the

outcome, not only its form.
Source: Prepared by the author.

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