Teaching artificial intelligence in secondary school: from development to practice

Problem statement . Currently, various global and national institutions promote mainstreaming artificial intelligence (AI) technology into training programs for school students. The effectiveness of introducing artificial intelligence into school curricula depends on four factors: 1) defining methodological foundations for creating educational content; 2) selecting and structuring appropriate learning content; 3) adapting the content to the needs of different age groups; 4) integrating the content into school programs. The current study provides theoretical foundations for generating learning content for AI lessons aimed at secondary school students and determines possible ways of integrating that content into school programs. Methodology. The empirical part of the study involved 225 secondary school students aged 11-14 (forms 5 to 9) as well as 125 teachers from comprehensive schools located in Moscow and the Moscow region. Analysis, synthesis, testing and sampling average methods were used. Results. The authors conducted a pilot testing of the developed educational materials, measured students’ AI-related skill and knowledge and processed the obtained data using the method of selective averages. The theoretical research conducted showed the leadership of artificial intelligence training in primary schools, mechanisms for developing learning outcomes in the field of artificial intelligence for primary school students, the opportunity to reveal the possibility of forming the content of artificial intelligence training based on various approaches. The goals and results of teaching the basics of artificial intelligence within the framework of basic school were determined. The content of training was formulated. Conclusion. The research is characterized by scientific and practical novelty, as it helps determine methodological grounds for teaching AI to secondary school students and proposes a detailed unit plan for an AI training course in secondary school

AI literacy in K‑12: a systematic literature review

The successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest in training citizens in AI. Education systems now need to train students at the K-12 level to live in a society where they must interact with AI. Thus, AI literacy is a pedagogical and cognitive challenge at the K-12 level. This study aimed to understand how AI is being integrated into K-12 education worldwide. We conducted a search process following the systematic literature review method using Scopus. 179 documents were reviewed, and two broad groups of AI literacy approaches were identified, namely learning experience and theoretical perspective. The first group covered experiences in learning technical, conceptual and applied skills in a particular domain of interest. The second group revealed that significant efforts are being made to design models that frame AI literacy proposals. There were hardly any experiences that assessed whether students understood AI concepts after the learning experience. Little attention has been paid to the undesirable consequences of an indiscriminate and insufficiently thought-out application of AI. A competency framework is required to guide the didactic proposals designed by educational institutions and define a curriculum reflecting the sequence and academic continuity, which should be modular, personalized and adjusted to the conditions of the schools. Finally, AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects, provided the curriculum is co-designed with teachersThis work has partially been funded by the Spanish Ministry of Science, Innovation and Universities (PID2021-123152OB-C21), and the Consellería de Educación, Universidade e Formación Profesional (accreditation 2019–2022 ED431C2022/19 and reference competitive group, ED431G2019/04) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS— Centro Singular de Investigación en Tecnoloxías Intelixentes da Universidade de Santiago de Compostela as a Research Center of the Galician University System. This work also received support from the Educational Knowledge Transfer (EKT), the Erasmus + project (reference number 612414-EPP-1-2019-1- ES-EPPKA2-KA) and the Knowledge Alliances call (Call EAC/A03/2018)S