Conference Program

The expansion of Atificial Intelligence within educational environments is transforming not only teaching practices but also the epistemic and cultural conditions in which subjectivity and educational experience are formed. Algorithmic infrastructures increasingly operate as forms of digital power: they filter information, anticipate interpretation, and structure the horizons within which knowledge becomes visible and meaningful. In this context, educational environments risk being reorganized according to predictive logics, optimization processes, and algorithmic plausibility, narrowing the epistemic space within which human judgment and pedagogical encounter develop.

This paper addresses these transformations by proposing a theoretical and methodological model for the education of teacher educators, seen as University professors responsible for preparing future teachers. The model is grounded in the assumption that teaching professionalism cannot be reduced to disciplinary knowledge alone, but emerges from the relationship between disciplinary knowledge and broader educational responsibilities. Within algorithmic environments, where discursive outputs simulate competence and understanding, this relationship becomes a crucial place of pedagogical reflection.

The theoretical framework integrates complementary traditions. Critical pedagogy conceives education as a dialogical practice oriented toward emancipation and development of critical consciousness (Freire, 1970; hooks, 1994). Theories of complexity and ecological development highlight the systemic interdependence between knowledge, context and person education (Morin, 1999; Bateson, 1972; Bronfenbrenner, 1979). Illich’s critique of institutional mediation emphasizes the importance of socially appropriable tools rather than infrastructures governing human action (Illich, 1971). At the level of professional practice, Schön’s theory of reflective practice highlights reflection-in-action and professional judgment as central dimensions of pedagogical responsibility (Schön, 1987).

These pedagogical perspectives intersect with debates in Philosophy of mind and AI studies concerning consciousness, intentionality, and simulation. Although AI systems generate linguistically persuasive outputs, several scholars argue that such systems lack the phenomenological and intentional structures characteristic of conscious cognition (Searle, 1992; Chalmers, 1996; Tononi, 2004; Faggin, 2024). From an educational perspective, this introduces a significant epistemic risk: discursive plausibility may be mistaken for genuine understanding, producing new forms of epistemic opacity within learning environments.

From a methodological perspective, the framework adopts participatory research-formation, seen as a shared reflective process in which teacher educators analyze practices while producing pedagogical knowledge. Through collective inquiry, disciplinary knowledge is reinterpreted in relation to three interconnected dimensions of educational responsibility: responsibility for meaning (humanistic dimension), responsibility for knowledge (scientific dimension), and responsibility for action (technical dimension).

Within algorithmic environments characterized by predictive design and informational filtering, the model identifies a central pedagogical task: cultivating discernment in situations where the distinction between simulation and understanding becomes increasingly difficult. From this perspective, opacity, vulnerability, and unpredictability do not merely represent limitations of technological systems, but become educational resources capable of interrupting the logic of algorithmic optimization. Pedagogy thus takes the form of resistance not through rejection of artificial intelligence, but through recovery of the irreducibility of human judgment, the unpredictability of the educational encounter and the openness of subject formation: teacher education can counter anticipatory control and preserve education as a space in which human experience remains an event rather than a programmed outcome.