Abstract:

The rapid expansion of Big Data, data science and Artificial Intelligence (AI) is reshaping economic and social systems and redefining essential competencies for the 21st century. Data literacy, statistical reasoning and informed decision-making have become core skills for meaningful participation in contemporary society. This raises the question of whether existing mathematics curricula adequately reflect digital transformation priorities.

The aim of this study is to examine the alignment of Latvian mathematics curricula from primary to upper secondary education with digital transformation goals, focusing on the presence of competencies related to AI and large-scale data analysis.

A qualitative curriculum content analysis was conducted to examine alignment with digital transformation priorities. The analysis covered the grades 1–9 curriculum, the general-level upper secondary course and the advanced-level upper secondary course. Learning outcomes, thematic content and overarching “big ideas” were analysed to identify explicit or implicit integration of AI, Big Data and data science concepts.

The analysis shows that AI and Big Data are not explicitly included as distinct content areas at any educational level. However, all programmes emphasise competencies that underpin data science. Students are expected to analyse and interpret datasets, calculate statistical indicators, evaluate probability-based situations and use digital tools for modelling and data representation. The general upper secondary curriculum explicitly states that data can be mathematically processed and analysed to support informed decision-making. The advanced-level course further strengthens statistical reasoning, probability theory and mathematical modelling in diverse contexts.

Although foundational data-related competencies are systematically developed, the absence of explicit references to AI and Big Data indicates only partial alignment with digital transformation priorities. The findings suggest a need for targeted curriculum updates and strengthened teacher preparation to ensure that mathematics education more directly addresses emerging technological realities and supports comprehensive digital competence development.

Keywords:

Mathematics education, curriculum analysis, digital transformation, data literacy, Artificial Intelligence (AI), Big Data.