Conference Program

This contribution uses the antinomical formula (Bertìn, 1968), the methodological pivot of pedagogical problematicism, to investigate two possible postures regarding the use of generative artificial intelligence devices within the school environment and teaching-learning practices, with the aim to overcome both of them towards a solution both ethically and technologically grounded. On the one hand, we have "reliance" as a posture that does not primarily involve critical and reflective thought, but rather it's focus on automation of the practices and knowledge of both educators and learners when using generative artificial intelligence systems. This pole is well represented by the discipline known as learning analytics (Siemens & Long, 2011). On the other hand, the pole of "support" highlights how it is possible to use devices with a critical and reflective approach, not fully entrusting aspects of teaching-learning practices to algorithmic outputs, but intervening where necessary, negotiating meanings, power, and knowledge. This second pole is represented, for example, by minority studies in critical education and technology (Selwyn, 2014).

According to its epistemological framework, pedagogical problematicism posits that the polarities analyzed within the antinomical dialogue possess both positive and negative elements subject to observation and analysis. These elements, therefore, must be taken into consideration with the aim of overcoming the two positions in favor of a synthesis that renounces dogmatic and ideological aspects in favor of greater pedagogical proximity between the positions. The point, therefore, is not to value "support" solely at the expense of "reliance", nor viceversa. This debate runs parallel to the polarized and polarizing positions of the excessively naïve techno-enthusiasts and the neo-Luddites, who would aspire to an almost total return to the analog; it is believed, in this case as well, that both positions have, from a pedagogical perspective, elements to be valued that are useful for the reflection set forth here. Finally, a recent and exploratory perspective will be examined which, thanks to its theoretical framework, overcomes the impasse that these two positions often bring with them. The reference is to the theory of “artificial agency” proposed by Luciano Floridi (Floridi, 2025). This theory, in its originality, does not banish computers nor eminently (all too) human qualities to the disadvantage of synthetic data but, on the contrary, enriches the discussion and represents in nuce a desirable future.