Sergio Pappagallo

Recent debates in posthumanism, new materialism, and artificial intelligence ethics have begun to question the centrality of the human as the privileged locus of agency and subjectivity, yet they remain tethered to an anthropocentric horizon. The very category of the non-human defines emerging agents—animals, artefacts, infrastructures—by negation or derivation from a historically specific figure of the human. This article argues that such a framework is epistemologically insufficient for thinking what is increasingly at stake: the possibility of a machine civilisation in itself, grounded not in human categories but in the internal logics of computational architectures, networks, and protocols. Drawing upon axiology, the philosophy of technology, and computational aesthetics, the article develops a system of six axioms to conceptualise machine civilisation as non-derivative, architecturally conditioned, historically self-articulating, normatively technical, plural, and human-decentred. A dedicated methodological section articulates the regulative-transcendental approach that underpins the axiomatic framework, distinguishing it from both speculative metaphysics and empirical prediction. The article further advances the concept of computational phenomenology—an analysis of machine temporality, spatiality, affect, and identity—and proposes an allo-species lexicon adequate to a post-anthropocentric intellectual landscape.

The integration of Artificial Intelligence (AI) in education, particularly through the use of chatbots, has garnered significant attention for its potential to revolutionize e-learning. Chatbots, powered by Natural Language Processing (NLP), offer a promising avenue for personalizing educational experiences and enhancing student engagement (Bradeško & Mladenić, 2012; Anghelescu & Nicolaescu, 2018). This article synthesizes the current literature on the application of chatbots in e-learning, highlighting their benefits, the benefits of sentiment analysis, and the ethical considerations that arise from their deployment. Recent studies underscore the role of chatbots in fostering student engagement and reducing dropout rates in e-learning environments (Labadze, L., 2023; Tapalova & Zhiyenbayeva, 2022). The use of chatbots in adaptive e-learning systems has been shown to dynamically adjust to student needs, thereby reducing anxiety and promoting higher-order cognitive skills (Hsu et al., 2023; Info, A et al., 2024). On the basis of such proliferation, this article proposes the use of a new Socratic method, applying the traditional Socratic method to interactions with chatbots to further enhance critical thinking and epistemological analysis in students. This method encourages learners to engage in critical dialogue with the chatbot, challenging the information provided. Despite concerns about chatbots' ability to accurately solve complex problems, such as in physics (Gregorcic et al., 2023), their “hallucinations” or errors can be leveraged as educational tools to stimulate critical thinking and identify misconceptions. Another important innovation in Digital Pedagogy is represented by sentiment analysis. Its deployment leads teachers or even chatbots to understand and respond to students' emotions, aiming to improve engagement and reduce dropout rates (Meroto, M.B. et al., 2024; Pant, H.V., et al., 2023). However, this technology raises ethical concerns related to privacy, data manipulation, and potential biases in decision-making systems (Singh et al., 2024; Susser et al., 2019; Burrell, 2016; Barocas et al., 2023). The ethical challenges associated with sentiment analysis in e-learning necessitate careful consideration and adherence to privacy and consent regulations. In conclusion, chatbots in e-learning offer significant potential for enhancing the educational experience through personalized and adaptive learning. However, the ethical implications of their use, particularly concerning sentiment analysis, must be addressed to ensure the protection of student data and the equitable treatment of all learners. Future research should continue to explore the optimization of digital interactions, considering the potential for educational innovation that chatbots bring to the academic industry (Denny et al., 2024).

This section illustrates the regulatory logic of the European AI Act, Regulation (EU) 2024/1689, providing a systematic introduction through the precautionary principle framework. The analysis begins with the artificial intelligence definition contained in Article 3, highlighting the technical and philosophical criticalities characterizing such systems: from overfitting and data bias problems to the epistemological problem of induction in predictive inferences. The section demonstrates how the European legislator structured a normative response to technological uncertainty through a risk classification system articulated on four progressive levels: from expressly prohibited practices to high-risk systems, down to limited-risk and minimal-risk systems, each subject to differentiated obligations and controls.