AI as a companion in education and healthcare

What we can really learn from each other’s practice

Generative AI is often discussed in terms of efficiency, automation and time savings. But if you look more closely, you see that the real value of AI lies elsewhere. Not in replacing people, but in supporting learning, reflection and professional practice.

In education we have discovered this step by step over the past few years. And those very lessons turn out to be surprisingly relevant to other sectors, such as healthcare. Not because education is “ahead” of healthcare, but because education acts as a testing ground in which it becomes visible what works, and what doesn’t.

From tool to companion

An important insight from education is that AI only adds value when it is positioned not as a smart tool, but as a companion. A digital thinking partner that helps to structure, mirror and deepen, without taking over professional judgement.

Think of:

• interim feedback on work or reports
• support in reflecting on choices and trade-offs
• help with formulating questions, goals and next steps

As soon as students or professionals experience that AI enriches their thinking rather than replacing it, the conversation changes. Fear gives way to curiosity. That tipping point is crucial.

Reflection and feedback at the core

In education we see that AI is particularly powerful when it is used around feedback and reflection. Not at the end of a process, but along the way. For example by:

• feedback agents that help students improve their work
• reflection questions that make thinking explicit
• bringing structure to complex assignments

The same logic applies to healthcare, where professionals work every day in situations of high complexity, time pressure and responsibility. There, AI can support reporting, handovers and evaluations of practice. Not by enforcing protocols, but by helping with careful recording and looking back.

AI literacy is not a switch you flip

A common misconception is that AI adoption is mainly a technical matter. In reality it is about AI literacy. That means not only knowing how a system works, but above all understanding:

• when to use AI and when not to
• how to critically assess AI output
• how to keep your responsibility as a professional

In education we have learned that resistance to AI often stems from uncertainty. The unknown is unloved. People need room to experiment, ask questions and learn together. Only then does ownership emerge.

Involve professionals from the start

An important lesson from both education and healthcare is that AI should not be “rolled out”. Professionals should not be confronted with systems that have already been designed without their input.

Sustainable AI implementation requires:

• dialogue about needs and concerns
• room to think along and help design
• transparency about what AI does and does not do

AI only works well in organisations where learning, reflection and professional autonomy are taken seriously.

Dealing consciously with Big Tech

In education there is also a growing awareness that convenience comes at a price. Many popular AI tools are commercial systems from outside Europe, whose terms and data use can change. That calls for conscious choices, especially in public sectors.

This conversation is not about rejecting technology, but about staying in control. About transparency, privacy and public values. Education and healthcare in particular have an exemplary role to play here.

What sticks

Perhaps the most important lesson is this:

AI is not a solution looking for a problem. AI amplifies what is already there.

In organisations where reflection, feedback and learning are central, AI can be a powerful ally. In environments where that room is lacking, AI mainly magnifies existing tensions.

So the challenge is not technical, but professional and pedagogical in nature. That applies to education. And just as much to healthcare.

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