Artificial intelligence can make an important contribution to improving the quality of teaching, particularly with reference to approaches of holistic type. The complexity of teaching requires approaches to improvement which consider the complex relationships between the elements which compose it (subjects, processes, means), over the time and in the context in which it is realized. The purpose of the work is to present the reasons that led to the conversion of an innovative holistic approach to higher education (L’Acolto) in algorithms for artificial intelligence. In this sense, the work attempts to highlight how artificial intelligence can make an important contribution to the continuous improvement of teaching quality, with a holistic approach to higher education. In this sense it is highlighted the impact that this produces on a specific reference context and on the broader social context.

Holistic approach to higher education and artificial intelligence. Social implications

Ida Verna
;
Gianluca Antonucci;Michelina Venditti
2019-01-01

Abstract

Artificial intelligence can make an important contribution to improving the quality of teaching, particularly with reference to approaches of holistic type. The complexity of teaching requires approaches to improvement which consider the complex relationships between the elements which compose it (subjects, processes, means), over the time and in the context in which it is realized. The purpose of the work is to present the reasons that led to the conversion of an innovative holistic approach to higher education (L’Acolto) in algorithms for artificial intelligence. In this sense, the work attempts to highlight how artificial intelligence can make an important contribution to the continuous improvement of teaching quality, with a holistic approach to higher education. In this sense it is highlighted the impact that this produces on a specific reference context and on the broader social context.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/722934
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