Side by Side – The Human Factor in the Age of AI – Strategic Framework
Machine Said So
Projects Objectives
What We Aimed
Machine Said So brought together 22 young participants from Romania, Bulgaria and Türkiye, supported by three group leaders and two facilitators. The project invited participants to explore artificial intelligence, not to fear it or trust it blindly, but to understand how it influences information, decisions, creativity and everyday life.
This Youth Exchange was designed to strengthen young people’s critical thinking and decision-making by improving their ability to understand data, question digital information, and use basic AI tools in a responsible and informed way.
To achive these, we specifically aimed for 3 things.
Objective 1
To develop critical thinking skills by enabling participants to analyse data, recognise bias, and assess the reliability of digital and AI-generated information.
Objective 2
To increase digital and AI literacy through hands-on exploration of simple AI tools, focusing on how they work, their limits, and their ethical implications.
Objective 3
To support personal and professional development by helping participants identify how data and AI skills can be used for learning, employment, and active participation in society.
Our Learning Journey
Project Results
Machine Said So was based on non-formal education and experiential learning. Instead of approaching artificial intelligence only through theoretical presentations, participants were encouraged to experiment, ask questions, compare perspectives and test different digital tools.
Activities combined international teamwork, creative challenges, simulations, debates, research, storytelling and structured reflection. Participants were not expected to have a technical background. Their personal experiences, curiosity and willingness to explore were the starting points of the learning process.
Working in international teams gave them the opportunity to examine the same questions from different cultural and personal perspectives. Facilitators supported the process while leaving space for participants to investigate, create, make mistakes and develop their own conclusions.
Reflection was an important part of the programme. Individual exercises, small reflection groups and shared discussions helped participants identify what they had learned, how their views were changing and how they could apply their new competences beyond the mobility.
Our Learning Journey
Project Activities
Mapping Our Digital Lives
The journey began with something familiar: the digital world participants experience every day. They looked at how technology is already present in the way they communicate, learn, work and spend their free time.
This personal starting point helped them notice habits they rarely questioned before. Which tools genuinely help them? When does technology make life easier, and when does it begin to make choices for them? As they discovered new AI tools, participants returned to these questions and reflected on how they wanted technology to fit into their lives.
Questioning What We See
As the exchange continued, attention shifted from using technology to questioning the information it produces and promotes. Participants explored how facts, opinions and interpretations can become mixed together, especially in digital spaces where convincing content can travel faster than accurate information.
By examining incomplete and sometimes conflicting versions of the same events, they experienced how easily a story can change depending on who tells it and what they choose to include. This led to deeper conversations about bias, misinformation and the responsibility each person has to verify information before accepting or sharing it.
Looking Behind the Technology
From their own digital habits, participants moved towards understanding what happens behind the screen. Together, they explored how artificial intelligence has developed, what algorithms do and why AI-generated answers are not always as neutral or reliable as they may appear.
Experimenting with different tools made these ideas more concrete. Participants discovered useful possibilities, but also encountered limitations, mistakes and unexpected results. The challenge was no longer simply to find another application, but to decide which tool suited a particular purpose, and whether its output could actually be trusted.
Discovering the Human Factor
One of the main discoveries of the project was that AI does not work independently from human input. Participants saw how small changes in instructions, context or perspective could lead to very different responses.
They gradually understood that working with AI involves much more than knowing what to type. It requires a clear intention, relevant knowledge and the ability to recognise when a result is incomplete, misleading or unsuitable. This brought the discussion back to the human factor: the judgment, creativity and responsibility that technology should not replace.
Exploring Questions Without Easy Answers
Not every conversation led to a simple conclusion, and that was part of the learning process. Participants discussed difficult questions about the use of AI in education, creativity and justice.
Should AI influence legal decisions? Does using it for creative work change the meaning of authorship? Should students be allowed to use it during examinations? By researching, defending different positions and listening to opposing views, participants learned to look beyond immediate reactions and consider the consequences of each choice.
Turning Ideas into Solutions
Towards the end of the exchange, participants brought together what they had learned and applied it to challenges from everyday life. Working individually or in teams, they explored how AI might contribute to possible solutions and transformed their ideas into digital prototypes.
Creating something of their own required more than using an AI tool. Participants had to identify a need, make choices, test their ideas and explain the reasoning behind them. Presenting their work and receiving feedback also helped them recognise what could be improved.
The process brought together the main elements of their journey: curiosity, critical thinking, creativity and human responsibility.
Our Learning Journey
Project Results
Participants created five teams, and then used what they had learned to give shape to five simple digital ideas. Of course, the final results are not finished applications, but prototypes that offered a first look at how AI and digital tools could respond to familiar challenges related to studying, planning, social life and personal organisation.
TENC: The Clothing Website
Choosing what to wear can take time, especially when we forget what we already have in our wardrobes.
Our idea was to create a digital wardrobe where users can upload photos of their own clothes and organise them by category and colour. They can then combine these items into outfits, save their favourite looks and plan what to wear on different days or occasions.
Type of results: A visual website prototype
Study Automation
Studying becomes difficult when we do not know what we already understand and where we need more practice.
We created a prototype that starts with the user’s own learning material. The user uploads a PDF containing class notes or information about a subject, and the application generates ten questions based on it.
After the user answers the questions, the prototype identifies the topics they understand and those that need more attention. It then creates a personalised study plan that can be downloaded and added to Google Calendar.
We also prepared a practical guide for students who want to create a similar study process using free digital tools.
Type of results: An app prototype, a presentation and a practical guide
Logic-Driven Social Event Matching
Making plans with friends can be complicated. We may like different things, live in different places or simply be free at different times.
Our idea was to create a platform that helps groups find events they could attend together. It would compare shared interests, available time, distance and community ratings before recommending an event.
We also wanted users to understand why they received a particular recommendation. Instead of relying on an unexplained result, they could see the information used to identify the best match.
Type of results: A concept for a social event platform
Momentum: The Art of Starting
Sometimes we know exactly what we need to do, but taking the first step still feels difficult.
Our idea was to create a digital tool that helps users begin a task without overwhelming them with another long plan or complicated productivity system. The tool would focus on the immediate obstacle and suggest one small, manageable action that the user could take.
We presented the problem and our proposed solution through a concept video.
Type of result: Digital concept
Presented as: Video
Project Backpack
After a busy week at school, our digital files can become a mess. Notes, screenshots and unnamed documents accumulate in Google Drive, and finding the right material becomes increasingly difficult.
Our idea was to create a tool that helps students organise this digital files. It could identify different types of documents and help users arrange their learning materials more clearly, making important documents easier to find.
We used a video to introduce the problem and show the idea.
Type of result: Digital Concept
Presented as: A video
Testimonials
What do they say?
Project outcomes in numbers
Project Evaluation
Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.
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