Purpose: The article presents a bibliometric analysis of studies in the artificial intelligence field, with the principal source of scientific articles selected being the academic platform Scopus. Design/methodology/approach: The content of 3365 open access research articles has been taken into consideration from 2019 and 2024 years. The search documents related to “artifical intelligence” “in” “education” issue in title, abstract and keywords. The analysis was performed using the VosWiever program. Findings: The study showed that AI assesses students' skills and requirements using machine learning, and then utilizes the findings of that analysis to develop and disseminate personalized or tailored information that improves learning via increased retention and uptake. AI improves learning for students by providing them with possibilities for experiential or hands-on learning, particularly when paired with other technologies like virtual reality, 3-D, gaming, and simulation. Research limitations/implications: The first limitation of the study is a result of the papers and reviews that were selected that deal with artificial intelligence. Since a broad variety of scientific fields are included in the field of artificial intelligence, findings may vary if publications from other domains are completely taken into account. Consequently, one should use extreme caution when extrapolating the study's conclusions to the vast domain of artificial intelligence. Another limitation is the research timeline (2019-2024); future results may vary since we anticipate that new topics, concepts, and techniques will emerge in the expanding field of artificial intelligence, which will significantly change the outcomes of our study. Finally, since the study's data came from Scopus, it's possible that this research is impacted by some of Scopus's restrictions. Practical implications: The manuscript can be a guide for universities on what students need for AI as well as how to improve the didactic process. Originality/value: Manuscript fills the gap in the analysis of what is the main role of AI in the education area from the student life-cycle and university effectiveness.
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