[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.

[pdf] [www]

Note: GENERATING

Abstract: The number of students who demand high quality education is growing continuously. Targeted, efficient education becomes increasingly important. Digital teaching formats combined with artificial intelligence offer promising opportunities and provide insights to develop seminal educational systems. In an ideal world the necessary data mining is integrated in those approaches and does not require sensors, surveillance or the close supervision of teachers. This review paper investigates the current state of research regarding actual applications of AI in educational learning concepts together with a focus on individual learner characteristics data. Within the study, 1.025 scientific papers from Scopus where screened and filtered. 67 papers were finally classified and evaluated. The review takes a close look at identified application categories such as the educational level of learners, academic subjects considered, learning environments used, types and objectives of the AI approaches, as well as a detailed examination of the underlying data. The actuality of the “AI in Education” topic is clearly visible in the growing number of publications. A substantial proportion of applications focus on university education with an accumulation in STEM subjects. Often, supervised AI approaches are used which focus on the prediction of learner performances. Data-wise, we see a lot of similarities in the approaches together with opportunities for improvement in terms of transparency and standardization.