Conference Program

As global competition over quantum technologies intensifies, science diplomacy is becoming increasingly entangled with geopolitical rivalry, raising concerns about its legitimacy and inclusiveness.Science diplomacy has often been recognized as an instrument of high politics rather than a form of participatory governance because of its traditional state-centered negotiation and expert-driven coordination methods.Under the conditions of advanced technological competition, an underexplored question arises: how can democratic forms of deliberation and learning be incorporated into science diplomacy?

A theoretical approach to explore the potential democratization of science diplomacy is presented in this paper by exploring quantum diplomacy games as an emerging analytical entry point.A critical focus of the paper is on initiatives such as the quantum diplomacy game developed by the CERN Open Quantum Institute, which conceptualizes serious games not merely as capacity-building or training tools but as experimental and reflective spaces where alternative governance logics can be simulated and examined.It is through the construction of counterfactual future scenarios characterized by technological scarcity, systemic interdependence, and global risk that these games offer participants the opportunity to engage simultaneously with scientific, political, economic, and societal issues.

It is the purpose of quantum diplomacy games to facilitate multi-role participation rather than prioritizing a single state interest or technological rationality. This involves actors navigating their overlap of responsibilities as scientists, policymakers, industry representatives, and civil society organizations.The objective of this design feature is to disrupt the conventional separation between technical expertise and political judgment, encouraging deliberation, negotiation, and compromise as necessary conditions for collective problem-solving.This approach embeds democratic practices such as dialogue, transparency, and mutual learning into the mechanics of decision-making rather than treating them as external ideals.

According to the paper, quantum diplomacy games can contribute to rethinking democratic practice in science diplomacy by analyzing publicly available project documentation, game design principles, and current scholarship on science diplomacy, democratic governance, and serious games.As part of these efforts, power is being reconfigured by utilizing non-traditional resources including data access and ethical standards, procedural transparency has been foreemphasized through iterative discussion and reflection, and conceptual space has been created for non-state actors to engage meaningfully in highly technical policy domains through iterative discussion and reflection.

There is no claim in the paper that quantum diplomacy games democratize science diplomacy in practice.Instead, it argues that they serve as exploratory tools that facilitate democratic negotiation, make it visible, discussable, and effective within complex technological governance frameworks.As a practice that can be explored and refined rather than as a fixed value that needs to be asserted.

Digital transformation has reshaped every domain of society—including early childhood education (ECE). While digital tools such as microscopes, cameras, and projectors have long been part of preschool pedagogy, the rise of artificial intelligence (AI) presents a new paradigm. Unlike passive tools, AI systems—especially those equipped with large language models (LLMs) or moral reasoning capabilities—act with a degree of autonomy and influence that raises unique ethical, cognitive, and cultural concerns (Floridi, 2018). A child’s interaction with AI becomes a meeting of intelligences: one intuitive, flexible, and embodied; the other logical, predictive, and coded.

This intersection becomes particularly critical in early education, a period marked by intense neurological, emotional, and relational development. The introduction of AI at this stage—through storytelling robots, personalized books, or emotion recognition systems—requires careful scrutiny not just in terms of safety and efficiency but in light of deeper philosophical and developmental assumptions. From an intentional-anthropological perspective, the child must be understood as a person–body unity structured by intentionality rather than reducible to computational cognition (Basti, 2012). Are these tools supporting or replacing relational bonds? Are they fostering exploration or reinforcing algorithmic conformity?

Moreover, AI technologies are not culturally neutral. Large language models, for instance, are often trained on data that reflect dominant languages and value systems. A striking example: 93% of GPT-3’s training data was in English, raising concerns about embedded cultural bias and value conflict (Johnson et al., 2022). Such imbalances risk marginalizing multilingual children or misrepresenting cultural values in non-western contexts.

Despite these risks, professional standards for ECE educators rarely include training in AI ethics or digital governance. As a result, there is a growing gap between technological innovation and regulatory readiness. The prospect of Moral Artificial Agents (MAAs)—autonomous systems capable of ethical decision-making—further complicates the picture, requiring a shift in governance from technical oversight to ethical discernment.

To respond to these challenges, this contribution proposes a foundational rethinking of educational governance in light of AI. We argue that any integration of AI in early childhood must begin by revisiting the anthropological assumptions that underlie our systems: What is our image of the child? What kind of person do we believe the child is? Drawing from the dual anthropology developed in Italian pedagogical and legal traditions, and from the relational conception of the child articulated in the Reggio Emilia approach (Malaguzzi, 1998), we offer a framework for ethical AI governance grounded in intentionality, relationality, and human dignity.

I begin by outlining three major anthropological problems that shape educational models: the mind–body relationship, the role of community, and the ultimate purpose of education. I then explore the ethical and cognitive challenges of AI integration, propose Reggio Emilia as a relational countermodel, and conclude with governance recommendations.

This contribution aims to bridge philosophical anthropology, neuroscientific insight, and concrete policy proposals—offering a vision for AI integration that enhances the humanity of the child. In this sense, educational AI governance must aim to safeguard and promote the child’s right to flourish, understood as the full realization of human potential.

This abstract presents a workshop developed with an early childhood education class (children aged 4 and 5) at a private school in Porto Alegre, Rio Grande do Sul, Brazil. The pedagogical proposal integrates research, maker culture, educational robotics, and Artificial Intelligence (AI), drawing on an approach inspired by Creative Learning (Resnick, 2017).

The project emerged from the children's investigative question, “What is automatic?” Initially, the group developed hypotheses about what might be automatic in the human body, identifying involuntary actions such as blinking, heartbeats, and hiccups (Rinaldi, 2024). The investigation then shifted to the built environment, culminating in the question: “Is the elevator automatic?” From this problem, the children collaboratively designed and constructed a manual elevator, exploring its mechanisms and modes of operation. The desire to transform it into an automatic one inaugurated a new phase of the process: the introduction of Artificial Intelligence (AI) (Russel & Norvig, 2010) as a concrete possibility for experimentation.

We then conducted a workshop to discuss, in accessible language, introductory notions of automation and basic ideas related to AI through hands-on, playful, and embodied activities. Children designed and built personalized toy automata made from laser-cut MDF (Martinez & Stager, 2013), incorporating their own paper-based characters into the mechanisms (Wilkilson et al., 2014). These creative constructions served as the physical interface for subsequent interactions and generated high levels of engagement, requiring careful pedagogical mediation to guide the activity's flow.

With the character ready and inserted into the automaton, the child moved on to the next step of the workshop. Using the micro:bit CreateAI platform, each child trained three distinct movements for their character: fast, regular, and stop, with their gestures. In the technical setup, a micro:bit captured the children’s physical gestures and transmitted commands via BLE to an ESP32 microcontroller, which controlled a DC motor in the automata.

The experience allowed the children to perceive, in a practical and embodied way, that the automaton’s behavior depended on the examples they provided. By testing and adjusting their movements, they developed an initial understanding that automatic does not mean something that happens on its own, but rather something that can be taught, adjusted, and transformed. Instead of formalizing technical concepts, the proposal prioritized the lived experience of cause-and-effect relationships between human action and machine response.

The results indicate that the contextualized integration of AI in Early Childhood Education is feasible when anchored in concrete, playful, and embodied experiences. The children formulated hypotheses, revised strategies, and collectively celebrated outcomes, exercising agency and authorship. AI was presented as an explorable language rather than as something magical or inexplicable, fostering an approach in which children actively participated in decision-making, tested hypotheses, negotiated interpretations, and understood technology as something open to human intervention.

By engaging with the initial question of what is automatic, the children expanded their understanding through experimentation: the automatic can be constructed, tested, and modified. The experience suggests that, when mediated by investigative practices centered on children’s agency, AI can be meaningfully integrated into early childhood experiences.

The growing presence of digital technologies in educational contexts raises critical questions about the pedagogical conditions through which they are meaningfully integrated into classroom practices. In particular, the potential introduction of AI-based conversational agents in school settings calls for reflection on how their use can be structured so as to support students’ comprehension processes and strategic engagement with text without reducing learning to exclusively individual interactions.

This contribution presents an instructional intervention developed across multiple fourth-grade primary school classrooms aimed at supporting students’ text comprehension through a structured pedagogical cycle combining individual work on the text, collective discussion, and metacognitive reflection. The intervention draws on research on reading comprehension and on instructional approaches that emphasise guided mediation of students’ understanding during reading (Kintsch, 1998; Palincsar & Brown, 1984; Lumbelli, 2009).

Within this framework, the pedagogical cycle was implemented across all experimental conditions. In one of the conditions, interaction with an AI-based conversational agent was introduced as a specific modality of mediation during the initial phase of individual work on the text. Through guided prompts, students were supported in making explicit use of comprehension strategies such as prediction, clarification, questioning, inference, and summarisation. The outcomes of individual work were subsequently re-examined in whole-class discussions, in which students’ interpretations were treated as shared resources for collective meaning-making. This process was intended to situate individual comprehension within a dialogic learning context, allowing students to compare perspectives and progressively refine their understanding.

A further component of the instructional cycle involved structured metacognitive reflection, supported by a self-reflection tool designed to foster awareness of reading strategies and regulation of comprehension processes.

The intervention is framed within a quasi-experimental design involving four instructional conditions implemented in parallel classroom contexts, including a control group following regular instructional practices. This contribution explores the pedagogical conditions under which the integration of AI-based educational tools can be meaningfully situated within instructional practices that sustain the social dimension of learning. In particular, it examines how individual interaction with AI-based tools, when embedded in mediated instructional practices, may be articulated with collective interpretive processes in classroom contexts.

As artificial intelligence and digital technologies become increasingly embedded in everyday life, educators face the challenge of designing learning environments that support children’s critical and participatory engagement with these systems (Resnick, 2024). Artificial intelligence influences how knowledge is produced, while digital platforms shape social and cultural life, making such understanding fundamental for democratic participation in digitally mediated societies (Floridi, 2023). However, public debate often oscillates between fascination and fear, frequently portraying children as passive users at risk when interacting with technology (Gallese et al., 2025).

Grounded in a Reggio Emilia-inspired playful approach to learning, this contribution presents a pilot research project developed by Fondazione Reggio Children in collaboration with academic and technical partners. The project is the result of a multidisciplinary collaboration involving researchers, an atelierista, and an educator, whose complementary perspectives contributed to the design, facilitation, and interpretation of the learning experiences. The project emphasizes how children’s agency can be nurtured through inquiry-based and participatory experiences, exploring how learning environments can foster awareness of how AI systems operate, support democratic engagement, and develop critical thinking through playful learning.

The study adopts a qualitative methodology based on pedagogical documentation, reflective dialogues, observations, and final interviews. It developed through workshop-based activities involving children aged 7 to 12. The research explored how children engage with complex concepts through a discriminative AI tool and how this process supports reflection and dialogue on human and artificial intelligence.

Activities were designed to explore technological concepts through multiple expressive languages and embodied experiences, consistent with the Reggio Emilia emphasis on the “hundred languages”. Before interacting with AI systems, children and adults explored their knowledge about AI in order to construct new knowledge together, creating space for critical exploration. This was also an opportunity to deconstruct common societal narratives surrounding the relationship between children and digital technologies. The process of selecting datasets, engaging in trial and error, and recognizing that machines require human intervention to reduce errors, made visible the human work embedded in AI systems. Misunderstandings in interactions between humans and machines highlighted the subjective nature of interpretation and how understanding emerges through social exchanges.

The experience positioned AI not as a source of answers but as a tool to support questioning and metacognitive reflection. Children were encouraged to recognize how machines reflect human perceptions and intentions, while intergenerational exchanges among children, educators, researchers, and parents supported democratic knowledge construction.

Preliminary findings suggest that playful inquiry can foster critical reflection, enhance awareness of how AI operates, and support collaborative meaning-making. The research highlights how AI can become an educational opportunity when it supports, rather than directs, the learning process, preserving the centrality of the child as an active thinker and co-constructor of knowledge. This contribution proposes a pedagogical approach in which AI integration is conceived as a relational and reflective process, capable of fostering children’s agency and democratic participation in contemporary digital societies.