Conference Program

In contemporary educational systems, the growing availability of data has not automatically resulted in more democratic, inclusive, or reflective decision-making processes. While data are increasingly central to governance and accountability, they often remain confined to school leadership or external evaluators and, for much of the school community, opaque - difficult to access and largely closed to scrutiny, interrogation, or debate by those most directly affected by their use - thus limiting their potential to support collective sense-making and shared responsibility, while UNESCO (2021) advocates for “a new social contract”. Research has highlighted how data-informed decision-making can foster improvement only when embedded in participatory cultures and supported by adequate data literacy (Vincent-Lancrin et al., 2019). Within this framework, Reflex was developed by INDIRE as a systemic, participatory, and data-informed self-reflection tool designed to support schools in interpreting, discussing, and governing innovation processes in a democratic way. Reflex is grounded in a holistic conception of school functioning (EU, 2022), where innovation is understood as an emergent property of interconnected organizational, pedagogical, and cultural dimensions rather than as the result of isolated initiatives or technological adoption. The framework articulates ten dimensions of school functioning (governance, curriculum, teaching and learning, assessment, collaboration, internationalization, wellbeing, professional development, learning environments, and time management) which together offer a comprehensive lens for reading how innovation is embedded, sustained, and culturally appropriated within school communities. These dimensions draw on and extend existing international frameworks such as Innovative Learning Environments (OECD, 2017), DigCompOrg, and SELFIE (European Commission, 2018), while explicitly addressing gaps related to organizational learning, wellbeing, and temporal structures.
What distinguishes Reflex from other evaluation or monitoring tools is its explicit democratic orientation. The tool is designed to be periodically completed by multiple school actors (i.e. school leaders, teachers, and staff) thus fostering shared interpretation of data and collective reflection on evidence. Rather than producing rankings or prescriptive judgments, Reflex generates shared representations of school functioning that become the basis for dialogue, negotiation, and informed decision-making. In this sense, data are treated not as neutral indicators but as cultural artifacts that require interpretation, discussion, and contextualization within the school community. Within the panel’s focus on knowledge, data, and democracy, Reflex can be understood as a device for democratizing both governance and data literacy at the school level. By making organizational processes visible across multiple dimensions, the framework supports distributed leadership, reduces informational asymmetries, and enables schools to move beyond compliance-oriented accountability toward reflective and participatory governance. Moreover, the cyclical nature of the tool supports longitudinal monitoring, reinforcing organizational learning and shared responsibility for change. The contribution argues that fostering democracy in education requires not only participatory pedagogies, but also shared infrastructures for reading and governing complexity, where data function as a common language for reflection rather than as instruments of technocratic control.

The paper explores, from a theoretical and design-oriented perspective, the potential of Generative Artificial Intelligence (GAI) to support formative assessment (assessment as learning), with the aim of developing pathways that integrate teaching, assessment, and students’ self-regulation processes. Within this framework, assessment tasks are conceived not merely as tools for measuring learning outcomes but as intentionally designed opportunities for deep cognitive activation (retrieval, restructuring, and transfer of schemas) and for the development of metacognition and evaluative agency.

The proposed learning pathways are structured as sequences of progressive challenges, in which learners advance to the next task only after mastering the conceptual tools underpinning the previous one—a condition referred to as learning readiness—through forms of deliberate practice. Each stage of the training process entails the gradual construction of cognitive, strategic, and dispositional prerequisites through experimentation, debriefing, conceptualization, and the automatization and transfer of acquired knowledge and skills. The expected outcome of this pedagogical approach is the cultivation of learners capable of mobilizing increasingly complex cognitive schemas with mastery and autonomy.

The paper begins by examining the potential of classical Intelligent Tutoring Systems (ITS) and extends their possibilities in light of current GAI developments, with the goal of defining design guidelines for their application within formative assessment pathways. Specifically, it discusses how GAI systems can enable adaptive cycles of interaction between student and machine, whereby the latter proposes sequences of challenging tasks and monitors, in real time, the evolution of students’ knowledge, skills, attitudes, and strategies. These tasks are designed to engage learners in a wide range of cognitive processes—including the identification of key concepts and inconsistencies, comparison and classification, synthesis, and graphical representation—with instructional guidance provided by the system itself. Learner reflection is fostered through process narration, error analysis, the definition of quality criteria, and reasoned self-assessment of performance.

The proposal outlines three levels of integration:

1. Knowledge base design that codifies not only disciplinary content but also performance quality constraints, evaluative criteria, and observational indicators of progression in knowledge, skills, attitudes, and competences.

2. Dynamic learner modeling that tracks not only outcomes but also the evolution of underlying cognitive processes and mental dispositions relevant to learning readiness.

3. Tutoring strategies that adapt task difficulty, feedback type and timing, forms of metacognitive scaffolding, and opportunities for collaborative engagement, transforming each assessment moment into an opportunity for cognitive restructuring and internalization of self-assessment and self-regulation criteria.

In conclusion, the paper highlights key challenges and open issues, such as modeling complexity, fine-grained personalization, and ethical and privacy concerns, to guide future research toward a pedagogically grounded development of GAI-based intelligent tutoring systems.

In recent years, data literacy has been conceptualised as a core professional competence for teachers and school leaders, situated at the intersection of educational governance, school improvement, and evidence-informed decision-making. Yet, despite this growing recognition, the concept remains theoretically fragmented and unevenly operationalised.

The EVIDALI project (Evidence-Informed Data Literacy for Policy & Practice) addresses this gap by providing a conceptual and empirical foundation for Data Literacy for Teaching, defined as teachers’ ability to transform information into actionable instructional knowledge and practices (Mandinach & Gummer, 2016). Through a PRISMA-guided review of 76 empirical studies published in 2011-2025, the project shows that only a minority of studies has a clear definition of “data”, while most privilege student performance indicators over qualitative, digital, demographic, and contextual sources (FBK-IRVAPP, 2025). This fragmentation is mirrored at the policy level, where national approaches to data literacy remain uneven and loosely institutionalised (European Schoolnet, 2025).

Integrating established frameworks on data use and instructional decision-making (Kippers et al., 2018; Schildkamp & Lai, 2013), the review identifies four core domains structuring teacher data literacy: 1) data use as a cyclical process (from problem identification to evaluation); 2) professional knowledge and skills; 3) internal factors such as beliefs and self-efficacy; 4) external conditions, including leadership and organizational structures.

Building on this analytical lens, the territorial experience of Città dell’Educazione translates these conceptual dimensions into an intervention and inquiry strategy. The initiative – a three-year programme implemented in four Italian cities (Turin, Genoa, Vercelli, Savona) across two regions and involving nearly 100 primary and secondary schools – initiated a structured listening phase based on document analysis and interviews with school leaders and staff. This exploratory phase revealed recurring challenges that closely mirror the gaps identified in the review: difficulties in identifying reliable data sources; limited competence in survey design and indicator definition; insufficient analytical skills; weak reporting practices; and limited integration between data use and strategic planning.

In response, Fondazione per la Scuola designed a modular, practice-oriented professional learning programme aimed at strengthening schools’ capacity to collect, analyse, interpret, and communicate data for educational planning. The programme combines theoretical inputs with applied work on real school datasets, including INVALSI data and internally generated data, and is organised through flexible modules covering survey design, data analysis, reporting and evaluation. A structured monitoring system accompanies the training, including participation tracking and a pre–post questionnaire assessing changes in data use, knowledge of data sources, interpretation skills, and attitudes towards data-driven practices. For school leaders, the instrument also measures perceived self-efficacy and aspects of educational leadership related to data use.

By connecting the EVIDALI conceptual synthesis with a territorially grounded experience, this paper argues that data literacy should be understood not merely as an individual technical competence, but as an organisational and cultural infrastructure that enables informed and accountable decision-making within school systems (Mobilio, 2025). The experience of Città dell’Educazione shows how data literacy can move from policy aspiration to embedded professional practice, contributing to a more reflexive and democratically oriented model of school governance.

This paper explores how democratic school governance and distributed leadership drive innovation within education systems and promote learning for democracy. Drawing on data from UNESCO's Global Education Monitoring Report Lead for Learning (2024) and its regional edition, Lead for Democracy: Latin America (2025), it analyses how sharing decision-making power among teachers, students, families, and communities helps build stronger educational outcomes and democratic skills.

The report finds that traditional top-down models of school leadership no longer adequately address today’s complex educational challenges. Collaborative leadership styles such as distributed, participatory, or democratic approaches, empower schools to harness the collective expertise of their communities, set shared goals, encourage cooperation, and support meaningful learning. These practices are deeply rooted in Latin American educational culture, even when not formally recognized by policy.

Through comparative policy analysis and case studies from various Latin American countries, the presentation will examines how governance structures, leadership training, and avenues for participation influence schools’ ability to operate as democratic institutions. The evidence indicates that distributed leadership boosts agency, involvement, and civic education, while also enhancing school improvement and organizational innovation.

Yet, there are notable gaps between policy intentions and real-world practice. Centralized decision-making, insufficient preparation for collaborative leadership, and lack of recognition for teacher and student leadership all limit the impact of democratic governance in schools.

Ultimately, this work contends that promoting distributed leadership will advance both democratic values and educational quality, positioning schools as spaces for democratic experimentation and systemic progress.

In an era of pervasive quantification, indicators and metrics increasingly shape how education, health, environment, and governance are understood and managed. Numbers are frequently presented as self-evident outputs, while the assumptions, classifications, omissions, and interests embedded in data infrastructures remain opaque (Markham, 2019). This contribution conceptualises statistical and data literacy as a democratic competence: the capacity to interpret, evaluate, and use quantitative information in collective decision-making, while reflecting on how data are framed and mobilised in institutional communication and public reasoning (Gal, 2002; Watson, 2006).

Grounded in critical pedagogy (Freire, 1970; Giroux, 2011) and scholarship on datafication and algorithmic governance (Markham, 2019), the paper approaches quantitative knowledge as a situated socio-political artefact rather than a neutral tool. A persistent challenge for education is that statistical ideas are often experienced as abstract, anxiety-inducing, and disconnected from lived experience (Garfield, 1995; Ben-Zvi & Garfield, 2004). We argue that theatre-based and role-based pedagogies can support epistemic agency by making data practices visible, experiential, and contestable in a shared learning space—where uncertainty, inference, and evidence become matters for collective deliberation (Braund, 2015; Lee et al., 2015).

We introduce the Theatre of Statistics framework, which brings together classroom drama—immersive, participatory, curriculum-based practices—and science theatre—performances for wider audiences—showing how theatre and drama can enhance statistical and data literacy by transforming abstract concepts into embodied, affective learning (Ødegaard, 2003; Bhargava et al., 2022). We operationalise the framework through two prototypes: a theatre-based format for younger learners (in development) and a climate-negotiation live action role-play in which participants represent countries with conflicting interests and use indicators, graphs, and sources to build claims, respond to critique, and negotiate collective priorities (Heras & Tàbara, 2014). The activity foregrounds evidence-based argumentation and the social consequences of reasoning with data under uncertainty (Wild & Pfannkuch, 1999), treating data literacy as a democratic practice of public justification and accountability.

Preliminary findings from our pilot survey suggest positive signals in perceived understanding and attitudes toward data, accompanied by high student engagement. Overall, the study illustrates how performative formats can connect knowledge, data, and democracy by making statistical reasoning observable, affordable, and pedagogically actionable within educational settings.

Expectations for schools to use data from multiple sources to improve the education they provide have been increasing globally (Verger & Skedsmo, 2021). What counts as data in education has expanded beyond standardised national large-scale assessments. Within the Italian school autonomy and accountability system, schools are expected to analyse and reflect on data provided by central administration as well as data produced locally to support pedagogical and organisational improvement.
This research investigates how schools engage with such data, including INVALSI national large-scale assessment tests, schools’ interim assessments and self-evaluation reports, as well as grades, demographic information, and more. The study asks how Italian schools and their actors conceptualise and engage with different types of data in practice, data use purposes and enabling conditions.
In 2016, Pastori and Pagani reported growing trust but difficulty in using national assessment data. Several years later, with a fully operational national school evaluation system, Italian schools appear to interact with data from multiple sources at varying levels of intensity. This study therefore examines the extent to which data-use processes and routines are present in Italian schools, what actors consider to be data, and what attitudes they hold towards its role in organisational and pedagogical improvement.
The analysis examines how school organisational and political contexts, individual factors, and the accountability system mediate data use, and whether and how data informs pedagogical and organisational decision-making. Attention is given to collaboration, collective reflection, and shared spaces and time within schools to understand how data are interpreted by school actors and how they contribute to decision-making processes.
The study adopts an understanding of policy as enacted, interpreted and reconfigured by local actors rather than neutrally implemented (Ball et al., 2012). It draws on conceptualisations of data use in education (Coburn and Turner, 2011; Spillane, 2012), engages with the notion of data-informed rather than strictly data-based decision-making (Schildkamp et al., 2019), and considers the potential role of data use in supporting equity-oriented practices (Datnow & Park, 2018).
Methodologically, this qualitative study draws on interviews (n=88) and focus groups (n=13) with principals, leadership teams, and teachers, and school document analysis in ten comprehensive primary and lower-secondary schools in Rome, of different socio-economic contexts.
Preliminary findings suggest a limited and heterogeneous presence of structured data-use practices. Organisational constraints and uneven levels of data literacy appear to constitute important barriers to sustained engagement with data. Often, data provide evidence for already known issues and are experienced as legitimising action to address them. More rarely, data are used to inform decision-making that fully exploits school autonomy to address equity issues. While schools express interest in using data to better understand student needs and inequalities, systematic processes supporting equity-oriented data use remain unevenly embedded in school practices.
This study contributes empirical evidence on data use practices in a Southern European low-stakes accountability context still relatively underexplored in the literature. It also advances understanding of how organisational conditions, data literacy, and collaborative practices shape data-informed decision-making and the potential use of data for equity-oriented purposes.