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The AI in education community has grown rapidly in the global north. [19] Currently, there is much hype from venture capital, big tech and convinced open educationalists. AI in education is a contested terrain. Some educationalists believe that AI will remove the obstacle of "access to expertise". [20]
AI literacy or artificial intelligence literacy, is the ability to understand, use, monitor, and critically reflect on AI applications. [1] The term usually refers to teaching skills and knowledge to the general public, particularly those who are not adept in AI.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
In this context, it is essential for education to adopt a humanistic approach, particularly in light of the increasing prominence of digital technologies. [7] An example of the application of innovative technology in education is the implementation of an AI-based tutoring system at an entry-level IT school in Pensacola by the U.S. Navy.
There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS.
An artificial superintelligence (ASI) is a hypothetical type of AGI that is much more generally intelligent than humans, [23] while the notion of transformative AI relates to AI having a large impact on society, for example, similar to the agricultural or industrial revolution. [24]
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This phenomenon has occurred in relation to every AI application produced, so far, throughout the history of development of AI. AI winter – a period of disappointment and funding reductions occurring after a wave of high expectations and funding in AI. Such funding cuts occurred in the 1970s, for instance.