AI: Our New Teammate in Everyday Life

Artificial intelligence (AI) is a type of computer program that can learn from examples. In simple terms, it learns by looking at lots of data and finding patterns. It can recognize patterns in pictures, sentences, or series of numbers. Only a few years ago, AI was something you saw in science fiction films or used mostly by scientists. Today, it is part of almost everything you do online, from choosing the next video or photo on TikTok to recommending your next song on Spotify or helping search engines, like Google, guess what you are looking for.

AI is also helping people solve real-world problems (Figure 1). In schools, AI tutors help students learn new languages or practice maths. In hospitals, doctors use AI to detect diseases earlier and more accurately. In the environment, AI helps firefighters predict wildfires and protect endangered animals. In cities, AI manages traffic lights and improves transport safety.

Because AI can now work alongside humans, it is changing not only what we do but how we do it, especially when we collaborate. Imagine your class working on a science project. Some students collect data, others write the report, and an AI tool analyses the results to find patterns that would be hard for people to spot on their own. The project becomes a mix of human and machine teamwork, where the group (people and AI) can learn and make decisions together. This mix of human creativity and machine intelligence shows that working together with AI can help teams think better and solve problems more effectively.

When Many Minds Think Better Than One

Have you ever noticed that some teams seem to come up with better ideas than any one person alone? That is because when different people work together, sharing their diverse ideas, skills, and ways of thinking, they can think better as a group. Scientists call this collective intelligence, which simply means “being smarter together”.

Collective intelligence is what happens when a group’s combined knowledge and creativity are stronger together than alone [1]. You can find it everywhere, from students working in groups to solve a problem, to scientists from around the world studying how to clean the oceans, to even a swarm of bees working together to find the best flowers!

These groups are smart not simply because they include many people or highly intelligent individuals, but because of the way they collaborate. Three things really matter: diversity, communication, and fairness. The best groups include people with different experiences, ideas, and ways of thinking. This diversity helps teams see problems from more than one point of view. Good communication, including sharing thoughts and listening carefully, helps connect everyone’s ideas. Finally, everyone should get a fair chance to speak and be heard, so no one is ignored.

When these ingredients are balanced, teams work better and learn more. Studies show that groups where people listen to each other and share ideas equally often solve problems faster and more creatively than groups led by just one “smart” person. You have probably seen this in your own class projects or games. Working together helps everyone learn, improve, and feel involved.

Humans + AI = Smarter Together

Now imagine your group project includes a new kind of teammate: a computer that can learn from information and help you think! When humans and AI work together, we call this hybrid collective intelligence [2]. This term simply means people and machines combining their strengths to solve problems together.

Humans and AI each have their own qualities. Humans are creative and thoughtful. We understand feelings, fairness, and context. We can imagine new possibilities and care about how our choices affect others. AI is fast and good with data. It can look through thousands of examples, find patterns, do many calculations, and suggest ideas very quickly. When humans and AI work together, they can achieve things neither could do alone.

Let us return to your science project. This time, your group is studying how pollution affects local plants. You collect samples and make observations, while an AI tool analyses the data to spot possible patterns or causes. The AI suggests explanations, but the group discusses them, checks if they make sense, and compares them with what you actually observed. By combining human curiosity, discussion, and critical thinking with AI’s speed and pattern-spotting ability, you build stronger and more reliable results.

But teamwork with AI also has risks. AI does not understand fairness, empathy, or right and wrong; it only follows patterns in data. If the data it learns from is missing information or unfair, the answers can also be wrong. Sometimes AI can hallucinate, meaning it makes up information or gives incorrect answers that sound convincing but are not true. Because AI often sounds confident, people might trust it too quickly and stop thinking critically [3]. In your science project, for instance, the AI might claim that pollution has no effect because it missed key data, or it might suggest an unfair conclusion that blames one group of people. If the team accepts the answer without checking, mistakes can spread.

That is why humans must stay in charge, question AI’s suggestions, and make the final decisions. Working with AI means thinking together with it, not letting it think for us. This means hybrid collective intelligence needs clear rules, fairness, and shared values to guide it. To do this well, we need to learn how to work together responsibly with AI.

Co-creation: Designing Smarter, Fairer Teams

Co-creation means working together to design or improve something that benefits everyone. It is not merely a matter of working side by side, but of collaborating around a shared goal, with clear rules and mutual respect.

You already learned about collective intelligence, which happens when a group’s ideas combine to create something smarter than any one person alone. But collective intelligence does not always make sure everyone is treated fairly or learns from the process. Sometimes it uses people’s ideas as pieces of a puzzle without noticing who is heard or who benefits in the end.

Co-creation builds on collective intelligence but extends it by putting people at the center of the process. Everyone helps shape decisions, learns from the experience, and shares ownership of the results. This means people do not just work together, they also grow together, gaining skills, confidence, and stronger connections.

This human-centered approach matters even more when AI joins the team. The goal is not only to use AI for smarter solutions but to make sure humans learn, improve, and stay responsible while working with AI. Three important ingredients make co-creation possible [4] (Figure 2).

First, as we just mentioned, collective intelligence (good teamwork) is necessary to encourage different people to contribute equally. Diversity, open discussion, and reflection make the group think better together than anyone could alone.

The second important ingredient is scientific rigor (good thinking). Co-creation follows structured methods: define a clear problem, test ideas, collect feedback, and improve over time. This scientific process helps teams avoid guessing and make decisions based on evidence.

The third ingredient that makes co-creation possible is good governance (fair rules), so that everyone knows how decisions are made and who is involved. Ideas are shared openly, credit is given fairly, and people feel safe to speak up. Fair rules also help decide how AI is used and when humans should step in.

To continue with our example, your science project grows into a real-world challenge: redesigning part of the schoolyard to make it greener and safer. Students use what they learned about pollution and plants to imagine new gardens and play areas. The AI tool suggests which species could improve biodiversity or how layouts affect sunlight and soil health. Then the students, teachers, and parents review the ideas; check which are safe, fair, and practical; and make the final decision together.

This is co-creation in action. The AI sparks ideas and provides evidence, but humans decide, ensuring the process stays creative, inclusive, and responsible. Everyone takes part, learns something new, and shares ownership of the final result, a project shaped by both human and machine intelligence, working for the common good.

Co-creating Wisely: Making AI a Good Teammate

Good co-creation with AI centers on mutual learning between humans and machines, while humans retain responsibility for decisions and keep people, fairness, and creativity at the core. The aim extends beyond building intelligent solutions, focusing instead on enabling teams to learn, evolve, and think more effectively together.

To remember how to co-create well, think of the Five Cs (Figure 3):

1. Curiosity—Explore and ask good questions. Be clear about what you want to achieve and try different prompts. For example, ask the same question in different ways and see how the answer changes. Compare the answers you get to see which makes sense. Curiosity helps you find new ideas and learn more.

2. Checking—Verify facts and fairness. AI can sound confident but still be wrong. Always ask, “Where did this come from?” and “Is it fair to everyone?”. Check important facts with books, teachers, or trusted websites. Humans must make the final decisions and keep AI accountable.

3. Creativity—Add your own imagination. Use AI as inspiration, not a replacement. Mix its ideas with your own style, experiences, and interests. Change, improve, or combine ideas instead of copying them. This helps you create something original.

4. Collaboration—Work with people, not just machines. Talk with classmates, teachers, or teammates about what the AI suggests. Discuss, improve, and decide as a group what works best. Share credit fairly between humans and technology.

5. Care—Use AI responsibly and kindly. Protect privacy, treat others with respect, and make sure everyone has a voice. Think about who might be helped or harmed by an idea. Use AI to help people and communities, not to harm or exclude anyone.

Conclusion: Learning to Think With AI

Artificial intelligence is becoming a regular part of how people learn, work, and solve problems together. In this article, we showed that AI works best when it is part of a team, not when it works alone. When people share ideas fairly (collective intelligence), combine their strengths with AI (hybrid collective intelligence), and follow clear rules and good methods (co-creation), they can achieve stronger, fairer, and more reliable results.

AI can be very useful: it helps analyse information quickly, suggest new ideas, and support learning and decision-making. For example, it can help students explore a topic faster or notice patterns in data. However, AI can also make mistakes, repeat biases, or invent information that sounds true but is not. Because of this, humans must always check facts, make the final choices, and think about who might be affected by the results.

Looking ahead, AI will likely become an even more common teammate in schools, science, and everyday life. Learning how to co-create with AI, by being curious, careful, creative, collaborative, and caring will help young people use these tools wisely. The aim goes beyond developing smarter technology, focusing instead on fostering stronger collaboration between humans and AI, so everyone can learn, grow, and contribute to the common good.

Glossary

Artificial Intelligence (AI): ↑ A computer system that can learn from examples and patterns, helping people with tasks like writing, drawing, or solving problems.

Collective Intelligence: ↑ The extra “brainpower” that appears when people share ideas and work together to solve problems better than anyone could alone.

Hybrid Collective Intelligence: ↑ When humans and AI combine their strengths—human creativity and machine speed—to think, learn, and create together as one team.

Critical Thinking: ↑ The habit of checking information carefully, asking questions, and deciding what makes sense before believing or using it.

Co-creation: ↑ A way of working in which people, and sometimes AI, design or improve something together, ensuring fairness, learning, and shared benefits for everyone involved.

Governance: ↑ It is how people agree on rules, make decisions, and share responsibility to organize a group and make sure things are fair.

Conflict of Interest

SC is co-founder and Chief Executive Officer (CEO) of the social enterprise Health CASCADE. QL is co-founder of the social enterprise Health CASCADE. NP is co-founder and Chief Scientific Officer (CSO) of People Supported Technologies (PSi). The authors declare that these affiliations did not influence the content or interpretation of this manuscript.

Acknowledgments

This study was funded by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement 956501. The views expressed in this manuscript are the author’s and do not necessarily reflect those of the funders.

AI Tool Statement

The author(s) declared that generative AI was used in the creation of this manuscript. AI tools (including ChatGPT and Grammarly) were used to improve the clarity and readability of this article. All content was reviewed, edited, and approved by the authors, who take full responsibility for the final version.

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