AI After 60: Try It, Think Critically, Have Your Say

Cover image: AI-generated illustration with fictional people; not a photograph of the Augsburg event.

Can I still get to grips with artificial intelligence after 60? I encountered that question at the Seniorentag 60+ event at Augsburg's adult education centre, the Volkshochschule, on 8 November 2025. My answer is yes. Start with something that interests you. Ask someone to explain an application, try it and check the results. This report on the Augsburg event provides the context for my talk.

I wanted to give people encouragement and a basis for forming their own opinions. Experiencing where a technology helps and where it falls short makes it easier to say what you expect from it. That takes understandable explanations and opportunities to try things out.

For me, the question therefore leads directly to participation in society. How do people gain access to new technologies? How can they influence their development? And what options remain for those who want to keep doing important things offline?

Your year of birth does not determine your openness

I know older people who are more open to new technologies than many younger people. That is a personal observation, not a statistic about generations. It reminds me to see people as individuals, with their own interests and circumstances.

Someone who has had little opportunity to explore digital applications may need more time, a clear explanation or someone alongside them for a first attempt. Good teaching makes room for that. It takes questions seriously and lets people decide for themselves what they want to use a technology for.

Looking at other age groups helps clarify the aim. My article on social-media regulation for young people also considers the relationship between protection, digital education and self-determined participation. Support needs to suit a person's age and circumstances. For me, enabling people to reach their own judgments connects the two debates.

Why I use dog training to explain AI

In my talks, I show an optical illusion: a wave pattern turns into the face of a young woman when you narrow your eyes. The familiar “dog or muffin” comparison also draws attention to how easily similar visual patterns can confuse us. These examples help me make pattern recognition tangible.

To illustrate one aspect of learning, I also use an analogy with dog training. Imagine a dog; let's call him Bello. He is learning to sit in response to a cue. His attention is drawn to a hand holding a small treat. The hand moves slowly over his head. Bello follows it, sits down and receives the treat along with praise. Dogs Trust also describes the basic principle of this training method.

Through repetition, a connection develops between the cue, the behaviour and a positive consequence. The hand movement can gradually be phased out. Eventually, the word “Sit” is enough. Bello has learned an association without having to follow a human explanation of the entire training situation.

For AI, the analogy illustrates how feedback can influence learning. In reinforcement learning, particular outcomes are evaluated, and the system is adjusted during training to make more highly rated outcomes more likely. The reward is a numerical quantity. The analogy explains this aspect of learning. Other AI training methods work differently, and it does not describe everything a dog can understand either.

The essential point for me is this: A convincing result alone proves neither human understanding nor subjective experience. AI can generate new texts, help with creative tasks and solve certain reasoning problems. Research shows, for example, improvements on verifiable tasks such as mathematics and programming, as well as support for writing stories. See DeepSeek-R1 in Nature and Doshi and Hauser on AI and creative writing.

A friendly turn of phrase therefore does not establish that the system has feelings of its own. And a confident answer does not establish that it is correct.

Start with something you want to do

In my Augsburg talk, I discussed smartphone translation features and help with research and planning, among other examples. I find concrete tasks like these useful starting points:

• Have a short message translated, then compare a translation back into the original language with what you wrote.
• Collect ideas for a day trip. Then check opening hours and transport connections with the relevant providers.
• Ask for an explanation of a term and compare it with a reliable source.

Choose something manageable whose result you can judge. An abstract discussion then becomes your own experience: What helps me? What would I like to work differently? Where do I encounter limits?

If getting started feels difficult, trying something together can help. Your questions should guide the process. Someone with experience can support those first steps.

The key question: Does that make sense?

“Does that make sense?” is a simple question I keep asking when working with AI.

Outputs that sound plausible but are false or fabricated are often called AI hallucinations. I also discuss this limitation in my article “Blumentopf der KI” — why Germany still has a chance in AI. What matters in everyday use is this: an answer's tone does not tell us whether the information is correct. A verifiable source is therefore more useful than confidence alone.

Does the answer fit what I know? Can I check its source? Is the information still current? What do I need to confirm before acting on it?

A well-written itinerary does not confirm an opening time or a train connection. A plausible explanation deserves a second look when important decisions depend on it. Let AI make a suggestion, then check what matters for your purpose.

Trying things out and remaining sceptical go together. First-hand experience helps you form a considered opinion: I want to use this application. This result convinces me. Here I need more information or a different approach.

Understandable explanations make participation possible

People should be able to help shape technology. That is why I call for an understandable explanation of publicly funded technology: Anyone receiving public funding to research or develop technology should explain to the public, on one page, what they are working on. I have also made this one-pager proposal in connection with the UN Digital Cooperation Day in New York.

I have also explained the proposal in a German-language post about understandable technology.

That page should address the questions people need answered to form an opinion:

1. What problem is being addressed, and who benefits?
2. Why was this technology chosen, and what alternatives are available?
3. What has demonstrably worked, and under what conditions?
4. What are the limits and unanswered questions?
5. What risks are involved, and who is responsible?
6. How can people affected by the technology ask questions and influence decisions?

An honest “We don't know yet” belongs on that page too.

The explanation opens the conversation. Participation also needs people who can be contacted and a clear account of what happens next: What changed because someone raised an objection? What stayed the same, and why? People must be able to contribute their wishes while decisions can still be influenced.

I address the international dimension of this task in my analysis of the United Nations' scientific AI panel. That article focuses on shared foundations of knowledge and how societies approach AI's opportunities, risks and unanswered questions. The call for an understandable one-page explanation is my own proposal.

For me, that is part of a participatory dialogue about technology and of Robotic & AI Governance. My analysis of Zuckerberg's AI vision leads to the same question: How do we connect personal opportunities with public responsibility?

Participation in society also needs offline options

I want to encourage people to try digital technologies. At the same time, I call for a right to participate in society through non-digital means. People who cannot or do not want to use a smartphone must retain access to essential information, services and opportunities.

If an appointment can only be booked through an app, or important information is available only through a QR code, there is a practical question to answer: How can someone access the service if they cannot use that route?

Digital services can open up access. Good design also takes account of personal conversations, understandable information and accessible offline options. In my view, someone's choice of device must not determine whether they can take part in society.

My message from Augsburg remains: Try the technology, check its results and form your own opinion. Make that opinion heard. Shaping our technological future concerns us at every age.

I had already addressed the topic in October 2024 in my own video on AI and digitalisation after 60.

Frequently asked questions about AI after 60