Generating content in digital education through Artificial Intelligence tools. A Bibliometric Review
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Abstract
The development of educational content using artificial intelligence algorithms has been implemented favorably to increase dynamics within and in autonomous activities as a methodological tool to improve cognitive learning qualities in students. Digital educational models in the wake of COVID-19 have accelerated scientific research on the use of artificial intelligence to support student academic performance, focusing primarily on computer-assisted learning dynamics. This study uses bibliometric analysis to evaluate the performance of these predictive mathematical algorithms on scientific learning over the last 50 years, demonstrating that this selflearning gap has generated indispensable content for education today. An introduction to artificial intelligence models as a tool for assisted learning is presented. The implications of digital education and the resources that can be used in learning dynamics and didactics are analyzed, generating variables that define their degree of use and level of learning through specialized review literature using the
Prisma2020 tool.
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