DOI: 
http://dx.doi.org/10.15293/1813-4718.2406.01
UDC: 
372.8
Karakozov Sergey D.
Доктор педагогических наук, Dr. Sci. (Pedag.), Prof., Director, Institute of Mathematics and Informatics, Moscow State Pedagogical University, sd.karakozov@mpgu.su, Moscow
Ryzhova Natalya I.
Доктор педагогических наук, Dr. Sci. (Pedag.), Prof., Leading Research Fellow, Research of Modern Directions of Education Development Laboratory, Federal State University of Education, sd.karakozov@mpgu.su, Moscow
SAMYLKINA NADEZHDA Nikolaevna
Доктор педагогических наук, Professor of the Department of Theory and Methodology of Informatics Education, Institute of Mathematics and Informatics, Moscow Pedagogical State University, nsamylkina@yandex.ru, Moscow
Samokhvalova Evgeniia А.
Senior Lecturer, Department of Applied Informatics in Education, Institute of Mathematics and Informatics, ea.samokhvalo-va@mpgu.su, Moscow

Variable Teaching of Artificial Intelligence and Data Analysis Basics in Computer Science General Education Course: Integrative Approach and Key Components of Methodology

Abstract: 
The paper prepared in the context of mainstreaming artificial intelligence (AI) school teaching describes a model of AI and data analysis variant teaching of schoolchildren in computer science class and its components based on the integrative approach. The main aim of this study is to provide scientific and teaching community with the results of a methodological research that was carried out in Institute of Mathematics and Computer Science (Moscow Pedagogical State University) to develop methodological support for teaching schoolchildren topics related to AI technologies in both basic and secondary general education organizations and basic or advanced computer science classes. The paper summarizes current experience in AI and data analysis school teaching, with an integrative approach being the main methodological approach to designing a course for schoolchildren. The paper proposes the structure and the components of a methodology of variant teaching of AI and data analysis that considers project and extracurricular activities possibilities and the requirements of the Federal State Educational Standard of Basic General Education and the Federal State Educational Standard of Secondary General Education. The paper concludes that the model that is designed in accordance with the requirements of Federal State Educational Standard and which is in a sense an “ideal” model for teaching schoolchildren AI and data analysis basics can be interpreted by educational organizations, for example, to design various learning trajectories that meet the personal needs of schoolchildren, the technical and methodological capabilities of an educational organization, and the personal character of students. Moreover, the paper emphasizes that this model for variant teaching AI and data analysis basics centered on an integrative approach is already supported with practical educational learning materials for schoolchildren (Basic General Education and Secondary General Education level) and can be used by computer science teachers in their classroom, extracurricular, and project-research activities, as well as in preparing high school students for AI and data analysis Olympiads.
Keywords: 
artificial intelligence; data analysis; variable training; integrative approach; Federal State Educational Standard of General Education; extracurricular activities; project activities
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References: 

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