R&D Unplugged

What happens when students use AI to study and where is the line between support and doing the thinking for them?

In this episode, we explore, together with our guest Liudmila (Mika) Piatnitcskaia, how students use ChatGPT during learning, what kinds of tasks they delegate to AI, and why the most “effortful” parts of thinking may be the ones we need to protect most. We also discuss an experiment with two AI tutors: one that gives answers more directly, and one designed to guide students toward the answer themselves.

Together, we ask how educational AI can be designed to support learning without replacing human thought.

The proliferation of complex and often conflicting digital information demands advanced cognitive strategies to discern source trustworthiness effectively. Yet, many readers remain on “autopilot” or favour personal justification over investigating who's behind the information. Let's explore how different educational strategies can break these automatic habits and foster a more analytical mindset. Drawing on a study of 179 university students, Gaston Saux and his team compared traditional teacher-led instruction with symmetric peer-tutoring. Their findings reveal that while both methods were effective, peer-tutoring's effect was clearer when participants justified their evaluation decisions in a specific task and reported their criteria for justifying their trust in online information in general. By bridging the gap between cognitive theory and classroom practice, this research aims to reflect on what interventions are more effective to promote critical source evaluation.

More about the speaker
Gaston Ignacio Saux is a researcher at the Argentinean National Scientific and Research Council (CONICET) and head of the “Text Comprehension and New Reading Contexts” program at CIPP-UCA (Buenos Aires). After earning a Ph.D. from the University of Buenos Aires and completing a CNRS-funded postdoctoral position at the University of Poitiers, he shifted his focus from inference generation to strategic source evaluation while reading in unreliable digital environments. Currently, his research team seeks to bridge the gap between basic cognitive research and effective, reflective reading practices.

In this episode, we explore a personal and professional journey beyond the code. Our guest, Colin de la Higuera, shares the realization that guiding research toward human values is not something that can be improvised; rather, it has become a vital field of research in its own right.

We move beyond the personal narrative to examine the critical interactions between Artificial Intelligence and the sharing of knowledge. With a primary focus on education, we discuss how we can intentionally steer AI to empower learners and democratize information, rather than letting it be defined by market forces alone.

In this episode, we explore creativity not as a mysterious gift, but as a process that can be studied, modeled, and nurtured. At the crossroads of computer science, cognitive science, and the learning sciences, our guest, Chloé Mercier, examines creativity as a situated and iterative learning process shaped by reflection and feedback.

Using computational approaches, we discuss how creative work can be analyzed without reducing its educational richness. These models help us understand how learners generate ideas from prior knowledge and environmental stimuli, revise their work, and gradually develop creative strategies.

Looking ahead, the conversation turns to writing education and the goal of fostering creativity alongside metacognitive awareness, not automating production. We also reflect on digital humanities and STEAM education, asking whether bridging the sciences and humanities could support more inclusive pathways and help address persistent social and gender inequalities.

In this episode, Jean Vassoyan explores a fundamental question in machine learning: how can we design “teacher” algorithms that effectively guide “student” agents?

Using a broad and abstract framework, he connects fields such as Curriculum Learning, Active Learning, and Reinforcement Learning, showing how they share a common mathematical foundation aimed at optimizing learning.

Applied to human education, this approach becomes Adaptive Learning, intelligent tutoring systems that personalize instruction for each learner. By viewing machine and human teaching through a unified lens, Jean invites us to rethink what it truly means to be a teacher and how this role can be defined and formalized in quantitative terms.

Empowering students: how to make digital transition an opportunity for inclusive education?

Empowering students is the cornerstone of inclusive education. To do this, we look at inspiring situations to understand the conditions and context that help teachers grow into inclusive practice. We ask what travels and what needs tailoring – what can be replicated, what must be adapted to local conditions and context, and what should not be copied. In particular, we will address the ambivalent role of digital technology: it can lock in inequities or, when guided on purpose, expand agency and support the sustainability transition.

More about our guest
Muriel Epstein is a researcher in the educational sciences at the EMA laboratory (Ecole Mutations Apprentissage) at CY Cergy Paris University, and is affiliated with the SAMM laboratory (Statistical Analysis and Multidisciplinary Modeling) at Université Paris 1 Panthéon-Sorbonne.

After working on school dropout and spatial segregation, her research focuses on digital technology as a possible lever for transforming schools toward greater inclusion. She is particularly interested in the risks of “crystallization” associated with digital technology and in the potential evolution of teachers’ roles and professional stance. She also works on methodological issues and on how qualitative and quantitative methods can be combined in social science research.

Developmental Coordination Disorder (DCD) affects roughly one child in every classroom. As common as dyslexia yet far less recognized, DCD can significantly impact a child’s daily school experience.

In this episode, Caroline Huron sheds light on the unique challenges faced by children with DCD and explores practical ways to support their learning and inclusion. Drawing from cognitive science research and real-world classroom practice, she highlights how collaboration between researchers, teachers, and children themselves leads to more inclusive and effective educational environments.

The jobs of the future are uncertain, and education is evolving fast. As we move beyond the industrial model toward competency-based learning, how can we empower human-centered education?

In this episode, we explore with Nathan Roberson how advancing 21st Century Competencies - like creativity, collaboration, and critical thinking - can make education truly transformative. He shares insights from Beyond Education’s development of innovative tools to assess and support these future-ready skills.

While the path ahead is full of tensions, it also holds immense opportunity. Together, we imagine an education system that not only prepares learners for work, but helps them shape a more sustainable and compassionate future.

In this episode of R&D Unplugged, we explore together with Prof. Shawn Ginwright how institutions can better support young people who have experienced harm. While trauma-informed care is a vital framework, he shows why it can be incomplete - often creating blind spots that limit healing. Blending research with personal stories, Dr. Ginwright highlights how racial and social inequality compound trauma and why a new vision is needed.

He introduces Healing Centered Engagement, an approach that expands beyond trauma-informed care by shifting systems from harm, discipline, and punishment toward restoration, hope, and well-being. This episode invites educators, practitioners, and policymakers to reimagine institutions as spaces not just of intervention, but of renewal - where resilience, joy, and thriving become possible.

Why do we reject fruits and vegetables just because they look imperfect?

Let's explore the invisible biases in our brains that drive food waste—and how learning can help shift them.
Drawing from six psychological experiments and a neuroscience study using eye-tracking, Carina Castagna shares how aesthetic imperfections trigger risk aversion in the brain’s more primitive regions, leading to the unnecessary disposal of edible produce.

But there’s a powerful insight: when people are encouraged to think abstractly, this wasteful bias drops significantly.

What does it means for education, behavior change, and sustainability transitions? The talk offers practical implications for teaching sustainability and reframing how young people—and adults—learn to care about food and waste.