Conference Program

This paper addresses how the teaching profession is being reshaped by platformisation, datafication and algorithmic governance in the current perma-polycrisis, where economic instability, democratic fatigue and ecological stress intersect. In marginal and inner-peripheral territories, digital transformation does not simply “modernise” education: it can operate as an extractive governance assemblage that captures value upward (data, attention, labour and resources), compresses local agency, and repositions teachers as compliance-driven executors. To conceptualise this condition, the paper mobilises the notion of Digital Mining Neo-Elitarism (DMN) as a soft extractive regime in which human actors co-govern with nonhuman agents (platform interfaces, interoperability standards, learning analytics dashboards, metrics, and infrastructural constraints). The core claim is that educational democracy depends on how we govern with technologies, not merely on how we teach through them.

The study reframes Universities’ and Research Institutions’ Third Mission (TM) as a more-than-human governance project: a democratic and redistributive infrastructure capable of enabling collective problem-framing, capability-building, and access to cohesion resources. In this view, the proposed Territorial Cohesion Ecosystem (TCE) functions as a counter-extractive, generative arrangement that redistributes agency and responsibility across teachers, third-sector organisations, local administrations, and public knowledge infrastructures. ECT is further anchored in a quintuple-helix orientation (government, economy, environment, research, citizenship) and is interpreted as a form of horizontal subsidiarity between pedagogy and politics, where democracy is both a curricular aim (“learning for democracy”) and an organising principle of educational processes (“democracy for learning”).

Empirically, the paper reports a longitudinal action-research intervention in Southern Salento (Apulia, Italy) involving 70 teachers from three Comprehensive Institutes, alongside third-sector organisations, municipalities, and research actors connected to CNR infrastructures. The intervention unfolded through three phases—activation, co-planning, and organisation—supported by governance devices such as Territorial Educational Pacts and Community Observatories. Methodologically, the study combines: Needs Mapping within Action Community Learning (deep listening oriented to social justice); participatory design (GOPP and World Café) for co-defining objectives and responsibilities; qualitative tools (focus groups, observation, questionnaires) to surface situated impacts such as teacher burnout and platform-related workload; and a research–training pathway aligned with DigiCompEdu 3.3.

Findings show a shift in professional posture: teachers increasingly reframed digital competence as democratic agency, moving from “executors” to agency architects who interrogate platform constraints, redesign learning toward participation and dialogic pedagogy, and foreground ethical responsibility. At ecosystem level, ECT-supported facilitation contributed to the institutionalisation of collaboration (pacts, observatories), clearer role distribution, and improved capacity to mobilise cohesion resources. A tangible indicator was the production of teacher-designed action plans and the translation of co-design outputs into concrete project artefacts (plans, pacts, funding applications). Finally, the opening of CNR research infrastructures to communities—including learners in educational poverty—reconfigured infrastructures as knowledge commons, enabling hybrid learning environments and new conditions for civic participation.

The paper contributes a transferable framework for counter-extractive co-governance in education, while noting limitations related to single-context evidence and the persistence of governance atomism; future work should develop explicit social-justice indicators and test replicability across inner-peripheral territories.

The article analyses contemporary narratives circulating in Danish universities guidelines, reports, and teacher training courses holding the promises of Generative AI (genAI) for knowledge systems automation, problematising the implications of these promises for the future of Higher Education. It draws on the concept of infrastructure theorised by Science & Technology Studies scholars, which refers tolayered, imbricated sociotechnical assemblages built into a system to provide for its smooth functioning – that is, an invisible ‘system of substrates’ (Star, 1999). The article seeks to expand this definition of infrastructure towards the inclusion of a more-than-human perspective in its conceptualisation. Being attentive to such a ‘non-anthropocentric ontology of infrastructures’ (Barua, 2021), in fact, can provide a relational analytic for grasping how infrastructures furnish ‘substrates’ for the university digitalisation agenda, and more specifically on what grounds the narrative about knowledge automation has been constructed.

Our analysis of more-than-human infrastructures includes: (i) public–private actors’ joint collaborations, where the lobbying of Big Tech, edtech companies, and data-extractive businesses is foregrounded; (ii) space-times of new academic practices with genAI (e.g. learning management systems with automated platform functionalities, computer labs where AI is naturalised as a ‘tool’ to speed up students’ academic work, or courses for teachers’ professional development where genAI is anthropomorphised as ‘sparring partner’); (iii) affective atmospheres generated by institutional expectations of upskilling for teachers and students alike; (iv) informal relational systems supporting the functioning of formal governance mechanisms (e.g. cultural norms and values, taken-for-granted assumptions, meanings, scripts, status differentiators, incentives, rewards, etc.); and (v) ‘field configuring events’ (Hinings et al., 2017), such as seminars, conferences, festivals, and courses on ‘responsible’ uses of genAI.

The analysis shows how the recent transition of Danish universities toward general acceptance of genAI tools for any purpose, including academic exams, can be understood as the last frontier of more-than-human infrastructural work that has established, also retrospectively, institutionalised techno-visions supporting the need for digitalisation – and increasingly automation – of knowledge practices. These results are put in dialogue with broader analyses of how ‘computing the incomputable’ (Parisi, 2016) has historically transformed the grounds of how both knowledge and cognition are conceived, making invisible the probabilistic nature of machine learning ‘reasoning’ and the selection of certain epistemologies over others. In conclusion, we argue that this more-than-human infrastructuring of the digitalisation agenda has contributed to conceiving academic knowledge as a set of finite outputs, learning as an efficient and linear process driven by the logic of speed (Atkinson & Flanagan, 2024), and research practices as optimised patterns’ extrapolation, anticipation, and modelling (Grimaldi, 2025). These aspects urge us to reflect on the need to reconfigure university practices for sustaining democratic futures.

Education governance is increasingly reorganised through artificial intelligence, data infrastructures and automated decision-making. Instead of treating AI as an instrument that enhances human policy rationality, this paper adopts a more-than-human approach to analyse how platforms, algorithms and data reconfigure agency, cognition and accountability across education systems. Building on Science and Technology Studies and new materialisms (Barad, 2007; Lemke, 2021; Massoumi, 2002; Whatmore, 2013) and education policy research on datafication and computational governance (Williamson, 2017; Gulson, Sellar & Webb, 2022), we operationalise three analytical tools (post-anthropocentrism, symbiosis and affectivity) to examine how AI participates in governing beyond the human.

Contemporary policy debates remain largely framed through human-centric categories such as fairness, efficiency and transparency (Selwyn, 2022), which presume that technology either supports or distorts human judgement. A more-than-human perspective instead treats governance as emerging from entanglements of humans, datasets, infrastructures, legal dispositifs and computational forms of cognition (Parisi, 2019; Hayles, 2014). AI does not simply execute policy decisions, instead it generates classifications and predictive logics that shape institutional futures and subjectivities. In this sense, governance is enacted through sociomaterial assemblages rather than located in human actors (Landri, 2018).

We deploy three empirical vignettes from England and Italy illustrate this shift, examining first the 2020 A-level grading algorithm in England, which was designed to standardise results after exam cancellations but disproportionately downgraded students from historically underperforming schools, thus reproducing structural inequalities embedded in training data (Williamson & Piattoeva, 2019).

In a second vignette we analyse the Italian algorithm allocating temporary teachers through automated ranking. This algorithm was introduced as a solution to bureaucratic inefficiency, but the system produced mismatches, labour insecurity and legal disputes. The third vignette instead contrasts these extractive configurations with IAQOS, a community-trained neighbourhood AI developed in Rome (Iaconesi & Persico, 2021). Through participatory design and open-source infrastructures, residents and students collectively shaped the system’s knowledge base.

Across the three cases, AI governance appears as a cognitive-affective formation rather than a neutral technical layer. Algorithms mobilise affects, including anger, anxiety, trust and attachment that reorganise institutional legitimacy and democratic participation. A more-than-human approach does not reproduce techno-solutionism instead it provides analytical tools to map heterogeneous agencies and to distinguish extractive assemblages from participatory and convivial ones. In doing so, it contributes to Critical EdTech Studies by reframing AI governance as a democratic and relational problem, aligned with ongoing debates on datafication, privatisation and the futures of education policy (Selwyn, 2022; Williamson et al., 2024).

This chapter aims to examine how knowledge about the learning of students with special educational needs emerges in a Facebook group of special education teachers that was analyzed for six months. The global massification of social networks (SN) and interactive pages, coupled with the importance of networking and collaboration in educational innovation processes has led to various studies on the importance of communities formed in—and through—SN in education and teacher training. However, studies on the use of social networking in teaching communities have not problematized what is being produced as pedagogical knowledge and its relation with contextual governance. Instead, they focus on the human use of these networks and see digital communities as eminently human groups. In this chapter, a sociomaterial approach was applied that assumes human agency and agency of the digital environment, which allows us to take the digital seriously, giving it its own agency and enabling us to analyze human and digital interaction as a particular space of production. This grants us to understand why in these spaces, which we will call assemblages, particular forms of pedagogical knowledge emerge and how it relates to the given governance. In this respect, the results of the paper indicate that knowledge appears in an uncritical way in the assemblage of the human and the digital and it is circulated d as neutral and overwhelming affections, and as the governance marketisatione. In short, the group functions as a repository of desires, emotions, doubts, information, and pedagogical material, which demonstrates the overload that teachers experience in their work and relation to institutional material and, at the same time, how this overload requires multiple responses to achieve success without problematizing what is shared.

This paper examines generative artificial intelligence, and particularly Large Language Models (LLMs), as epistemic infrastructures reshaping contemporary academic knowledge production. Situating the analysis within the broader transformation of universities - marked by performance metrics and pervasive technological integration - the study investigates how AI-mediated research practices intersect with questions of epistemic trust and the organization of academic work. Drawing on Science and Technology Studies, epistemology, sociology of science and computer science, in this paper AI is framed as a sociotechnical system, navigating between overly enthusiastic technodeterminism and ideological rejection.

First of all, we need to consider how LLMs, unlike human cognition, operate through large-scale statistical pattern recognition, generating their outputs through probabilistic correlations learned from vast data corpora; there is no access to semantic understanding, intentionality, causal reasoning or contextual judgment. For this reason, what appears as coherence is often the surface effect of statistical regularity rather than grounded comprehension, not considering LLMs structural vulnerabilities, such as susceptibility to data poisoning, sensitivity to prompt framing and generation of plausible but incorrect statements.

As these systems become embedded in core research practices - literature synthesis, hypothesis articulation, methodological design - two different dynamics emerge. First of all, if traditional academic “trust” is rooted in identifiable authorship, accountability and shared epistemic norms - necessitating agents capable of reflexivity and commitment - LLMs, by contrast, cannot assume responsibility for their claims, nor can they participate in the normative space of reasons that structures scientific debate. Secondly, if LLMs intervene directly on the rhythms of research - compressing tasks into near-instantaneous outputs - they carry significant consequences for the quality and depth of scientific work, because they could foster a culture of “epistemic impatience”, in which speed becomes a proxy for productivity and depth is sacrificed in favour of output volume. This dynamic is further reinforced by the institutional pressures already shaping contemporary universities - publish-or-perish logics and competitive funding regimes - which find in AI-mediated acceleration a seemingly natural ally. These processes reveal not only an epistemological challenge, but also a structural tension: the tools celebrated as researchers' “assistants” may erode the epistemic foundations upon which scientific knowledge is built and sustained.

These architectural and institutional tensions demand a calibrated response. Rather than advocating wholesale adoption or rejection, this paper argues for flexible regulatory frameworks preserving human oversight and responsibility, alongside concrete practices - such as disclosure of AI use and verification protocols - to rebuild trust in AI-mediated research environments. Equally important is how the growing integration of LLMs may itself offer an opportunity to critically interrogate the neoliberal turn of the contemporary university: by making visible the tensions between this system and the epistemic request for genuine scientific production, these tools show what is at stake when productivity displaces knowledge as the constitutive horizon of academic life.

Education and democracy are under threat worldwide.

The conservative turn, the decline of the power of the UN, the crisis of the EU, the genocide in Gaza, the war in Ukraine and Iran, and climate change are giving rise to a global polycrisis in which democracy is seen more as a constraint than a means to provide solutions to the complexities of our time. In that scenario, education is oriented toward the priorities of digital capitalism and the conservative agenda, thereby undermining the link with democracy. How to renew the relationship between education and democracy? How to reinforce a link that has been so important for the field of education studies, and for the onto-epistemology of modern education? In that paper, we address these important questions by engaging with the concept of more-than-human education governance (Landri & Peruzzo, 2026). The idea of more-than-human draws attention to the complex cognitive-affective assemblage of humans and nonhumans that emerges in the governance of education. The growing entanglements among humans, technologies and materials constitute a significant shift in the materiality of governance in education systems. Datafication, platformization, the rapid diffusion of generative AI and the experimentation with robots have made education policy and practice technologically dense and affectively charged. Numbers, rankings, apps, platforms, digital identities and software are now the constant companions of teachers, principals, students, parents, and policymakers.

The multiplication of agencies in education governance intersects with the theory of radical democracy (Latour, 2004; McDonnell, s.d.; Ranciere, 2004). Radical democracy invites an extension of equality and liberty, considering democracy not a state, but an open-ended process in which the ‘demos’ of democracy is expanded to many, and even to non-humans. Radical democracy assumes conflict and dissensus as key and ineradicable aspects of politics, challenging existing asymmetries, hierarchies, and elites. Similarly, more-than-human education governance sees the entanglements between humans and nonhumans far from frictionless and distinguishes between parasitic and mutual relationships, favouring the unfolding of convivial and joyful intertwinements between agencies. It therefore contrasts with the current depoliticisation and juridification of education by renewing the link between education and democracy through grassroots and participatory environments.