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Chapter Six

AI in Teaching and Learning

A smarter assistant, not a substitute for the teacher or the learner’s effort
How to use AI to build deeper understanding, better practice, useful feedback, and more
inclusive learning
Chapter question: when a ready-made answer becomes easy to obtain, how do we make
the learning itself visible and measurable?

Story: When the Answer Alone Stopped Proving Learning
In 2023, universities and colleges faced a new question after generative AI tools spread: if
producing a well-organized essay or a convincing answer has become easy, how do we

establish that learning actually happened? The Australian higher education quality agency
published a document on assessment reform for the age of artificial intelligence, not as a
call to chase the tool, but as a call to redesign the evidence that proves learning has occurred

[10].

The discussion returned in 2025 in a more practical form in a follow-up resource on enacting
assessment reform. Its central idea is that an institution is not served by general prohibition
rules or by automated detection tools. It needs tasks that make thinking visible: a draft, a

justification, a defense, a local application, a decision log, or a live performance that ties the
final product to the process that produced it [12].

This case is documented because it does not concern any individual student, but a wider
institutional shift. The problem is no longer that a student might use a tool. It is that some

older forms of assessment were measuring the final product more than they were measuring
understanding.

When AI can produce a good text, the educational question becomes: which mental effort

must remain the learner’s own work? And what evidence reveals that effort?
The answer therefore lies neither in banning AI nor in opening it without limits. It lies in
designing learning where the difference shows between someone who submitted an

attractive output and someone who built knowledge they can explain, apply, criticize, and
defend. A product may be excellent in form, but learning is only established when we see
the attempt, the path, the justification, and the transfer to a new situation.

This chapter therefore begins from a simple rule: in education, we do not measure the value

of AI by the number of texts, questions, and presentations it produces, but by what it adds
to the learner’s understanding, practice, independence, and capacity to think.

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