FROM THE ARCHIVE
Learning with AI should leave the learner more capable.
The new academic term is a useful moment to reconsider what assistance is for.
Retrospective editionPrepared for this archive on The edition date identifies its place in the weekly series.
As students and educators begin a new term in many places, I want to return to a basic question: what should someone be able to understand and do after using an AI tool? The answer should guide the task, the assessment, and the support around it.
Use assistance to reveal thinking
An AI tool can help a learner compare explanations, practise a method, or identify questions to investigate. The learning activity should still make the person’s reasoning visible. Ask them to explain the choice they made, check an important assertion, or discuss why an alternative approach might be better.
Give educators and mentors a clear role
People guiding learning need time to examine the tools and decide where they fit. UNESCO’s guidance on generative AI in education emphasizes human capacity and a human-centred approach. I would carry that principle into industry placements as well: supervision and feedback should support the learner’s development rather than focus only on the speed of the output.
Connect academic and practical standards
For Genellipse’s university relationships, I want a bridge between exploration and responsible delivery. A prototype can be a valuable learning result without being ready for production. Help participants understand that distinction and the evaluation needed to move further. A strong collaboration leaves students with better judgment and the company with a clearer understanding of the problem, including questions that remain open.
How Genellipse can help
Genellipse can help connect an academic learning objective with a practical data, software, or AI problem. Through a scoped university collaboration, participants can examine requirements, build and evaluate a prototype, and document what remains unresolved. The engagement should define supervision and learning outcomes alongside the technical work, with production adoption treated as a separate decision.
FROM PERSPECTIVE TO PRACTICE
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