Our network of schools is implementing a large-scale educational project aimed at developing key 21st-century competencies in students. When introducing learning objectives oriented towards studying artificial intelligence within the NIS Programme 2.0 curriculum, the content, methods, and outcomes of school education are transformed. The NIS Programme 2.0 curriculum includes updating educational plans by incorporating learning goals related to generative artificial intelligence for subjects in the natural-mathematical, natural-scientific, and humanities cycles. In an era of rapid digital technology development and the global rise in the importance of AI as a tool for knowledge, creativity, and professional activity, universal learning objectives for generative AI allow for the inclusion of competencies such as model interpretation, critical evaluation of AI content, data visualization, and collaborative problem-solving. Learning goals for generative AI allow for the inclusion of competencies such as model interpretation, critical evaluation of AI content, data visualization, and collaborative problem-solving across various subjects — mathematics, biology, economics, history, and language disciplines. Research shows that AI is becoming an interdisciplinary tool for expanding educational opportunities and strengthening the connection between schools and real technological and social challenges.

A structured coding matrix was used to quantify the presence of generative AI–related learning objectives, competencies, and instructional elements across subject curricula (mathematics, natural sciences, humanities, and language disciplines). Standardized assessment instruments and performance rubrics were developed to measure student competencies, including algorithmic thinking, data interpretation, critical evaluation of AI-generated content, data visualization skills, and collaborative problem-solving. Closed-ended questionnaires were administered to students and teachers to collect numerical data on frequency of AI tool usage, perceived competency development, digital literacy levels, and attitudes toward AI-supported learning. Quantitative analysis of assessment results, project scores, and task completion rates was conducted to identify trends and correlations between AI-integrated instruction and student learning outcomes.

The integration of AI is not limited to technical skills but acts as a systemic pedagogical innovation mechanism: from developing algorithmic thinking and digital literacy to fostering critical thinking, data skills, media literacy, and ethical responsibility.

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