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The state-of-the-art applications of artificial intelligence in distance education: a systematic mapping study

Gulnora JamalovaDepartment for the Coordination of Joint Educational Programs, Tashkent State University of Economics, UzbekistanFarida AymatovaDepartment for the Coordination of Joint Educational Programs, Tashkent State University of Economics, UzbekistanSayidolim IkromovEnglish Language Department, Tashkent State University of Economics, Uzbekistan
2022en
ABI

Аннотация

In this knowledge-based society, the increasing need of gaining knowledge has forced universities into the effective adaptation of distance learning technologies in their educational program so that they can satisfy a huge number of students who prefer to attend in distance learning mode. Advances in artificial intelligence technologies have also contributed to the development of distance learning programs as support tools in teaching and learning processes, closing the educational and organizational gap between students and teachers such as performing time-intensive tasks, providing constant feedback, and reducing disqualifications and dropouts in distance learning courses. In this contribution, this paper presents the state-of-the-art applications of artificial intelligence in distance education by using a systematic mapping approach that aims to identify, analyze, and classify the relevant literature about different artificial intelligence technologies applied in distance education environments. Based on research questions, and search and selection strategies, 60 candidate studies are identified and retrieved from four academic repositories. Afterward, they are further filtered through selection criteria and discussed to reach a consensus, leading to exclusion of 23 documents. The resulting 37 studies constitute the final pool of key publications that are classified and analyzed to build content taxonomy and provide the state of the art in artificial intelligence-supported remote education. This paper also identified research interest clusters with highlights for the term analysis of the textual & behavioral patterns of students, face recognition, and emotion recognition. The study concludes the paper with implications, limitations of the study, and future research agenda.

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