The paper explores the integration of Artificial Intelligence (AI) into socio-educational practices in Italy through the implementation of a research-action project called TEACH-AI. The authors present the impact of AI by presenting a three-dimensional theoretical framework that includes affordances-in-practice, professional capabilities and the sentiment of operators. The results of a questionnaire administered to 414 professionals show that AI is perceived as useful for information retrieval and idea generation but raises significant concerns about the impoverishment of expressive richness and privacy risks. Emotional analysis reveals ambivalence between curiosity/hope and mistrust/fear, highlighting the need to implement robust digital and ethical literacy to ensure responsible AI integration. The work opens future prospects in which AI should serve as a support for relationships, requiring human vigilance to mitigate the risks of algorithmic bias and excessive standardisation of procedures.

Exploring AI in Socio-educational Practices: A New Theoretical Framework

Matteo Adamoli
;
Michele Marangi
2026-01-01

Abstract

The paper explores the integration of Artificial Intelligence (AI) into socio-educational practices in Italy through the implementation of a research-action project called TEACH-AI. The authors present the impact of AI by presenting a three-dimensional theoretical framework that includes affordances-in-practice, professional capabilities and the sentiment of operators. The results of a questionnaire administered to 414 professionals show that AI is perceived as useful for information retrieval and idea generation but raises significant concerns about the impoverishment of expressive richness and privacy risks. Emotional analysis reveals ambivalence between curiosity/hope and mistrust/fear, highlighting the need to implement robust digital and ethical literacy to ensure responsible AI integration. The work opens future prospects in which AI should serve as a support for relationships, requiring human vigilance to mitigate the risks of algorithmic bias and excessive standardisation of procedures.
2026
978-3-032-31853-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11389/94837
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