MYP Learning Labs: Advancing Education

Dear parents, 

As you know, between September and October, students in M1, M2 and M3 spent two periods a week in the Learning Labs, a new space in the timetable dedicated to developing judgement, learning how to learn and looking at the future with curiosity. These periods were quite different from conventional lessons, as here, for the first time, we set aside a dedicated time and space so that students could focus entirely on a few key areas of learning, namely judgement and critical thinking, study skills, and the ability to look at the future and at the great ongoing changes in the world with both curiosity and a critical eye.  

Through these sessions, we wanted students to start developing a genuine sense of curiosity and the ability to work well with their peers, approach the shifts that will shape their future with a positive and open-minded attitude, strengthen the study skills that support their daily work and, not least, settle smoothly into the new school year, making the transition from one year to the next with a sense of wellbeing. 

With this article, we would like to share with you what your children have been working on over the past few weeks, together with some reflections from the professionals who led the sessions, and to show you our commitment to constantly updating and innovating our academic offer, so that it keeps pace with a rapidly changing world. 

Before bringing this phase to a close, however, we would like to introduce short Future Pathways sessions for M3 students already this school year, to help them discover their passions and strengths and begin to reflect on the choices that lie ahead, because we felt that this responds both to a real need among students and to a wish expressed by many families. We will share more details with you in the coming weeks.  

Why innovating. We want H-IS to remain at the forefront of education, with an up-to-date model that can respond to the needs of today’s world and of the world our students will grow into, so that when they graduate, they are truly ready for what awaits them beyond school. This is why we explore constantly pedagogical innovation with a twofold aim: to develop capabilities that traditional curricula do not always guarantee, (for example, judgement and a sense of purpose), and, at the same time, to strengthen academic success, since these very capabilities help students perform better across every subject, in their final examinations in applying to universities. Judgement in particular has become more important than ever now that artificial intelligence is part of students’ everyday lives, as the research we share below clearly shows. At H-IS we decided to advance our learning and teaching approaches, leveraging scientific educational research, and for this reason you will also find references to some of the scientific studies that informed our design. 

What students worked on. Over these weeks, students followed three main strands, each designed to grow with them from M1 to M3: 

• The Eight Shifts, led by Pierluigi Fasano, Professor of AI and Big Data at H-FARM College and senior advisor to NYUAD Vice Chancellor, which invites students to look beyond the present by exploring eight major transformations already under way, from fusion energy and space economy to quantum computing, engineered longevity and artificial intelligence,  New Materials & Synthetic Biology, Biological Compute and Distributed finance to understand the forces reshaping the world and their own place within it. After exploring the shifts, students also had the chance to experience first-hand what it means to make choices in uncharted territory through an interactive team game, in which each group had to take decisions about establishing the first colony on Mars. 

• Judgement Lab, led by Professor Emiliano Cori, where students learn to form their own judgement, grounded in truth, evidence and responsibility, and to express it clearly and convincingly. The guiding question evolves with age: “How do I know?” in M1, “What is the right thing to do?” in M2 and “What kind of person am I becoming?” in M3. 

• Study Skills, with Blessing Santander, devoted to building the habits that sustain learning over time, from organisation and time management to note-taking, memory strategies and preparation for tests. 

Alongside these strands, students also took part in sessions on wellbeing, with Cristiano Serafin, and on interdisciplinary projects, with the SPARX team, which focused on teamwork, on tackling complex problems and on using artificial intelligence thoughtfully and critically. 

What the research tells us 

None of what we offered was chosen on intuition or in pursuit of the latest trend: every activity responds to a need documented by research and draws on approaches whose effectiveness has been tested in peer-reviewed studies, because we believe that innovation in education is only worthwhile when it starts from solid evidence on how students actually learn.  

The starting point. The OECD’s PISA 2025 results show that, across OECD countries, average performance has fallen by 28 points in reading and 22 in mathematics since 2015, and that the decline in reading is linked above all to students’ ability to evaluate information, connect different sources and read critically. The same report finds that dispositions such as curiosity, perseverance and the willingness to ask for help are associated with stronger outcomes. 

Why judgement matters more than ever: learning in the age of AI. The effect of artificial intelligence on learning depends far less on whether students use it than on how they use it, and that in turn depends on the judgement, intentions and habits of mind they bring to it.  

The most robust evidence comes from studies with secondary-school students. Bastani et al. (2025) gave nearly a thousand high-school mathematics students access either to a standard ChatGPT-style interface or to a tutor designed to safeguard learning: unrestricted access raised performance during practice by 48% but led to a 17% decline once the tool was taken away, whereas the guarded tutor produced even larger gains in practice with no subsequent decline, since students without guardrails used the tool as a crutch and then performed worse on their own. The same pattern emerges with older students: in a randomised trial with university students, those who had studied with ChatGPT scored 57.5% on a surprise test 45 days later, against 68.5% for those who had studied without it (Barcaui, 2025).  

Conversely, when AI is designed around sound teaching principles the picture changes, and in a randomised trial at Harvard, students working with an AI tutor built on research-based pedagogy learned more, in less time, than their peers in an active-learning class (Kestin et al., 2025). A much larger study of Chinese secondary education points the same way: following 26,811 students in grades 7 to 12 over 30 months, Strömberg, Lei and Wu (2026) found that AI adoption raised homework scores by 18% and cut completion time by 30%, but lowered monthly exam scores by 20% within six months, with entrance-exam losses of 18–24% emerging only after about two years, while students who kept spending their own time on their homework suffered only small losses. 

The mechanism behind these results is not the technology itself, but the gradual handing over of thinking to it. Research on cognitive offloading shows that learners rarely notice when they hand their thinking over to a tool (Risko & Gilbert, 2016), and Gerlich (2025) finds this tendency strongest among the youngest users. Shaw and Nave (2026) go a step further and speak of “cognitive surrender”, the adoption of AI outputs with minimal scrutiny: in their experiments, participants’ accuracy rose by 25 percentage points when the AI was right and fell by 15 points when it was wrong, and those with greater trust in AI and a lower need for cognition surrendered most. In other words, people tend to inherit the machine’s errors precisely when they stop exercising their own judgement. Encouragingly, the same research shows that accountability and feedback reduce surrender from 73.2% to 57.9% and double the rate at which people successfully override a wrong answer, from 20.0% to 42.3%, and PISA 2025 likewise associates the critical evaluation of AI output, rather than its simple use, with better performance.  

This is why a principle that guides all our work is that students always make their own attempt before turning to a machine.  

What we did and why: 

• Judgement Lab → dilemmas and structured discussion. Working through ethical dilemmas in a guided way has been shown to measurably improve the quality of moral reasoning (Schlaefli, Rest & Thoma, 1985), while engaging seriously with opposing positions and working towards a shared synthesis has strong effects on both achievement and the quality of argument (Johnson & Johnson, 2009).  

• Artificial intelligence → a guide to reasoning, not a shortcut. In line with the evidence above, in the interdisciplinary projects AI was used as a tool that asks questions and guides reasoning rather than one that simply supplies answers. 

• Study Skills → learning how to learn. Decades of research on metacognition, the ability to monitor and regulate one’s own learning, show that it is a skill that can be taught (Flavell, 1979; Schraw & Dennison, 1994), and a meta-analysis confirms that it is linked to academic performance even once intelligence is taken into account (Ohtani & Hisasaka, 2018). 

• Wellbeing → feeling part of a community. PISA 2025 associates a sense of belonging and a positive school climate with better results, and well-designed programmes of social and emotional learning have been shown to improve both students’ personal skills and their academic achievement (Durlak et al., 2011). 

• Interdisciplinary projects → learning by doing. Meta-analyses show that project-based learning leads to better academic outcomes than traditional teaching, because students apply what they know to real problems and have to make it work (Chen & Yang, 2019). 

• The Eight Shifts and thinking about the future → the right moment. Neuroscience describes adolescence as a second sensitive period of brain development, in which the regions involved in self-regulation, social understanding and decision-making are still maturing and are especially responsive to experience (Blakemore & Mills, 2014): an ideal time, in other words, to nurture curiosity and critical thinking. 

How it went: in the words of those who led the sessions 

To give you a sense of how these first weeks went, we would like to share the accounts of the teachers and experts who worked with the students, starting with the two strands that, more than any others, anticipate the direction the school is taking. 

The Eight Shifts: reading change rather than being swept along by it 

The Eight Shifts was led by Pierluigi Fasano, who works on these topics every day with students and professionals, with the aim of helping young people read change rather than simply undergo it. The sessions begin with how science itself has evolved, from observation to theory, from theory to computation and now to artificial intelligence, and then move on to eight major transformations already under way: fusion energy, space as a new place of human activity, biological computing based on living cells, decentralised finance, new materials and synthetic biology designed with AI, quantum computing, health and longevity, and artificial general intelligence. Along the way students work on very concrete abilities: asking good questions, distinguishing between what has been demonstrated and what has only been promised, reasoning about consequences and discussing and deciding as a group. The awareness the team wants to leave them with is simple: technology opens up possibilities, but it is people who decide what to do with them, and in a few years those choices will be theirs. 

Why start at 11 to 14? Because these changes do not belong to a distant future: they have already begun and will accompany students throughout their adult lives. At this age young people are curious, they have genuine questions and they are not yet afraid of making mistakes, which makes it the ideal moment to get them used to looking at what is new with a critical eye, without naive enthusiasm and without fear. The point is not to anticipate university content but to offer students a way of reading the world, so that they learn to ask “How does it work?”, “What is it for?” and “What does it change for people?”, and can face a world in constant transformation with more tools and greater serenity. 

The eight shifts were chosen for three reasons: they are already happening, backed by real research and real investment, so they are not science fiction; each touches a different area of society, from energy and the economy to health and work; and each raises questions that are not only technical but deeply human, such as who owns space, what changes if energy becomes abundant, or what a machine capable of understanding the world really is. These are questions that bring together science, economics and ethics and that have no single right answer, which is precisely the point: the most important thing to teach is not the answer, but the way of asking the question. 

The most immersive moment was a team game in which students had to found a new city on Mars. A lesson can explain that resources are limited and that every choice has consequences, but a game makes students live it: they quickly discovered that they could not have everything, and had to decide how to use energy, space and resources, which rules to give themselves, how to handle the unexpected and how to reach agreement with the other groups. At that point the eight shifts stopped being topics and became tools for building a society, and students applied what they had discovered, negotiated, made mistakes and corrected course, before reflecting together on their choices in a final discussion. We are convinced that students learn far more from a decision they have taken and discussed than from a slide they have listened to. 

The quality of the students’ ethical reflections was impressive. When the discussion turned to cellular rejuvenation, nobody stopped at the spectacular side of the discovery, and someone immediately asked whether such an expensive treatment would only be available to a few, and what would happen to a society in which part of the population ages and part does not: questions that many adults never ask themselves. Students also showed a strong critical spirit: discussing investment in space, one group wondered whether those resources might be better used to make our own planet sustainable, not as a rejection of research but as a serious question about priorities. And they showed a real capacity for judgement: on artificial intelligence, they refused to accept that it must replace people in almost every job, asking instead in which cases it makes sense, in which it does not, and what remains irreplaceable in human work.  

This, as Pierluigi stresses, is not a course about technology but a workshop in thinking, in which students do not learn to use a tool but learn to reason about complex issues, express an argued opinion, listen to those who think differently and decide together, drawing on what they study every day as they read, calculate, write and present. He would also like parents to know that these topics, which sometimes worry adults, are approached by students with curiosity and great seriousness, and that when they are given the space to do so they ask surprisingly mature questions. 

There are three words he hopes students will take home with them. The first is curiosity, the desire to understand how the things that change the world actually work. The second is a habit of mind: faced with anything new, asking not only what it can do, but what it means for people and for society. The third is confidence in the future: at a time when the future is often painted in dark colours, the aim is to offer students a far more positive and motivating perspective for their studies, in which the future is not something that happens to them but something they can help to build. If they leave feeling that they have a say in it, the goal has been reached. 

Judgement Lab: “make haste slowly” 

In the words of Emiliano Cori, Head of School, who led the Judgement Lab. 

I chose to lead the Judgement Lab myself because, when I believe something matters, I find it difficult to remain at a distance from it. The starting point is an observation familiar to all of us: information is rarely more than a click away, and we are increasingly expected to have an opinion almost as soon as something happens, leaving very little time between encountering something and deciding what we think about it. I believe education has a responsibility to make room within that interval, giving students time to notice, to understand and to ask whether they know enough to judge. The expression that best captures this intention is festina lente, make haste slowly, because taking care at the beginning helps us see more clearly and act more effectively afterwards. 

In the sessions, students take the process of forming a judgement apart. They begin with a case study and write down their first decision; then further information is introduced, a circumstance changes or another person’s perspective comes into view, and what seemed obvious becomes more difficult. After thinking individually, working in pairs and discussing in groups, they return to what they wrote at the beginning and can see where their thinking has held and where they were too quick, so that the admission “I might have been wrong” begins to mean something grounded in their own experience. Along the way we keep returning to questions that sound simple until we try to answer them carefully, such as “What do I know?”, “What am I assuming?” and “What would give me a reason to change my mind?”, with the aim of developing careful reasoning together with intellectual humility: the confidence to make a judgement and the willingness to examine it again. 

I think we sometimes infantilise children precisely in the effort to make education accessible to them. Students of this age are capable of far more than we sometimes expect: they need support, such as language explained, an unfamiliar idea approached through an example and time to think before being asked to speak, but with that support they can enter demanding discussions with considerable seriousness, and we should be careful that our assumptions about their readiness do not become the limits of their education. There is pleasure in discovering that you can think about something difficult, and young people deserve to encounter that pleasure early. 

The students’ response has confirmed this. The youngest brought a vivacity I have very much enjoyed, made of bold questions, lively remarks and a joie de vivre that gave the discussions their own energy, while M3 students showed a more measured quality of questioning, built through careful discussion, listening to one another and developing a thought together. What these first encounters have shown me is how much depends on the invitation we make as teachers: when we take students’ thinking seriously, we give them a reason to take it seriously themselves. 

My hope is that, over the three years from M1 to M3, repeated practice will turn into dispositions, so that this learning becomes part of a person’s way of acting. We hope to see it across subjects, in friendships, in disagreements and in the decisions students increasingly make for themselves. This is the most ambitious part of the project, and understanding whether it is taking root will take time, the observations of colleagues across subjects and careful listening to students and to you, their families. 

The other sessions 

• Wellbeing, with Cristiano Serafin, invited students to reflect on the theme of belonging. Almost all of them feel they belong to their family, while belonging to their class group emerged as the least developed dimension, a valuable insight that tells us how actively the school needs to create the conditions for it to grow. When students pooled the personal strengths that each of them brings, they were surprised by how much the class as a whole has to draw on. 

• Interdisciplinary projects, with the SPARX team, offered each year group a thinking tool to keep using beyond the sessions: teamwork and communication in M1, through hands-on group challenges; Design Thinking in M2, with four sessions on “AI & Education” and “Community & Wellbeing” that ended with the presentation of a prototype; and critical thinking in M3, through an enquiry cycle in which AI was used to ask questions and guide reasoning rather than to provide answers. Students were so engaged that they often lost track of time. 

• Study Skills, with Blessing Santander, focused on organisation, time management and the planning of long-term projects. Many students discovered that they were already using study strategies without fully realising it, and that a good method is flexible and combines different approaches, and some asked for extra copies of the checklists to use at home. A suggestion for families: three short check-ins a week to review organisation and deadlines together, gradually moving from close supervision to lighter-touch support. 

Next steps 

This first phase has given us a great deal: the work produced by the students, the observations of the teachers and experts who accompanied them and, just as importantly, your feedback, which we are now gathering in a structured way from students, families and staff and which will guide the choices we make in the months ahead. 

• Listening to the students. We have called an assembly with all M1, M2 and M3 students, because we want to hear directly from them how the Learning Labs went, what they learned and what they found most valuable. The assembly is taking place on Friday, 13^th November.  

• The Learning Labs will return next school year, redesigned in light of what we have learned over these weeks and of what you have told us. 

• Future Pathways for M3. As anticipated before, we will introduce short Future Pathways sessions for M3 students already this year, to help them discover their passions and strengths and begin to reflect on the choices that lie ahead.  

• Involving you first. Before introducing any of the innovations we have in mind for the next school year, we will consult you, to share our ideas and hear your feedback. 

We are committed to communicating more clearly and to keeping you informed at every stage of this journey, because our goal remains the same: to guarantee your children consistent academic excellence and, alongside it, the tools they need to face the world ahead with confidence.  

With warm regards, 

Emiliano Cori, Head of School  

Antonello Barbaro, Director General 

References 

• Barcaui, A. (2025). ChatGPT as a cognitive crutch: Evidence from a randomized controlled trial on knowledge retention. Social Sciences & Humanities Open, 12, 102287. https://doi.org/10.1016/j.ssaho.2025.102287 

• Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), e2422633122. https://doi.org/10.1073/pnas.2422633122 

• Blakemore, S.-J., & Mills, K. L. (2014). Is adolescence a sensitive period for sociocultural processing? Annual Review of Psychology, 65, 187–207. 

• Chen, C.-H., & Yang, Y.-C. (2019). Revisiting the effects of project-based learning on students’ academic achievement: A meta-analysis investigating moderators. Educational Research Review, 26, 71–81. https://doi.org/10.1016/j.edurev.2018.11.001 

• Demszky, D., Liu, J., Mancenido, Z., Cohen, J., Hill, H., Jurafsky, D., & Hashimoto, T. (2021). Measuring conversational uptake: A case study on student-teacher interactions. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (pp. 1638–1653). https://doi.org/10.18653/v1/2021.acl-long.130 

• Duckworth, A. L., & Yeager, D. S. (2015). Measurement matters: Assessing personal qualities other than cognitive ability for educational purposes. Educational Researcher, 44(4), 237–251. https://doi.org/10.3102/0013189X15584327 

• Durlak, J. A., Weissberg, R. P., Dymnicki, A. B., Taylor, R. D., & Schellinger, K. B. (2011). The impact of enhancing students’ social and emotional learning: A meta-analysis of school-based universal interventions. Child Development, 82(1), 405–432. https://doi.org/10.1111/j.1467-8624.2010.01564.x 

• Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911. 

• Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. 

• Johnson, D. W., & Johnson, R. T. (2009). Energizing learning: The instructional power of conflict. Educational Researcher, 38(1), 37–51. 

• Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. 

• Kestin, G., Miller, K., Klales, A., Milbourne, T., & Ponti, G. (2025). AI tutoring outperforms in-class active learning: An RCT introducing a novel research-based design in an authentic educational setting. Scientific Reports, 15, 17458. https://doi.org/10.1038/s41598-025-97652-6 

• OECD (2026). PISA 2025 Results (Volume I): Future-Ready Students. OECD Publishing, Paris. 

• Ohtani, K., & Hisasaka, T. (2018). Beyond intelligence: A meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition and Learning, 13(2), 179–212. https://doi.org/10.1007/s11409-018-9183-8 

• Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. 

• Schlaefli, A., Rest, J. R., & Thoma, S. J. (1985). Does moral education improve moral judgment? A meta-analysis of intervention studies using the Defining Issues Test. Review of Educational Research, 55(3), 319–352. 

• Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19(4), 460–475. 

• Shaw, S. D., & Nave, G. (2026). Thinking — fast, slow, and artificial: How AI is reshaping human reasoning and the rise of cognitive surrender. Wharton School Research Paper, SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6097646 

• Strömberg, D., Lei, V., & Wu, Y. (2026). The generative AI learning penalty: Evidence from Chinese secondary education. CEPR Discussion Paper No. 21577. Centre for Economic Policy Research. https://cepr.org/publications/DP21577