Conference Program

The increasing integration of Artificial Intelligence (AI) into adult learning environments is reshaping not only pedagogical practices but also the very conditions of subject formation. Algorithmic systems function as cognitive and environmental architectures that anticipate interpretation, personalise content, and structure access to knowledge through opaque processes of prediction and datafication. In this context, adult learning risks being reduced to adaptive optimisation, narrowing the epistemic space in which autonomy and ethical agency can emerge.
This paper investigates AI-mediated adult learning through the conceptual lenses of educational opacity, subjectivity, and critical pedagogy. Empirically, it draws on nationally representative survey data produced by INAPP, examining the diffusion of AI-related training and AI-mediated learning practices among adults in Italy. The findings indicate rapid growth in AI-supported learning, particularly through personalised and micro-modular formats. However, access and meaningful engagement remain stratified by education, digital competence, age, and organisational context.
While AI promises personalisation and inclusion (Adarkwah, 2024; Cera, 2024), the data reveal a tension between expanded accessibility and diminished pedagogical mediation. AI-mediated learning environments tend to prioritise immediacy, performance, and predictive guidance, often marginalising dialogical interaction and collective reflection. In platformised labour contexts characterised by digital monitoring and algorithmic management (Gonzalez Vazquez et al., 2025; Cappelli & Rogovsky, 2023), adult learning becomes increasingly aligned with organisational optimisation rather than emancipatory formation.
Drawing on critical educational theory (Biesta, 2011; Slowey et al., 2024), the paper argues that opacity should not be conceived solely as a technological deficit requiring transparency, but as a pedagogical condition that preserves indeterminacy, vulnerability, and openness to the unexpected. In contrast to predictive educational design, opacity sustains the possibility of interruption and encounter—elements essential to subject formation.
AI-mediated adult learning thus presents a paradox. It expands access to knowledge while simultaneously constraining interpretative autonomy through anticipatory systems that pre-structure meaning. From the perspective of a democratic and socially just lifelong learning agenda (Stiglitz & Greenwald, 2014), the challenge is not to reject AI, but to develop pedagogical practices capable of resisting total optimisation and reclaiming educational spaces as sites of reflexivity and ethical responsibility. By integrating empirical evidence with philosophical reflection, this contribution advances an understanding of adult learning as a critical terrain in which algorithmic power can be interrogated and pedagogical resistance enacted.