Basic concepts of data literacy

Data literacy in practice includes the ability to:

• Recognise relevant data in a given context.
• Explain data sources and methods of collection.
• Analyse and interpret data to draw reliable conclusions.
• Apply data-based insights in planning and implementing teaching and research.
• Assess and act in accordance with ethical principles, including respecting privacy, protecting personal data, ensuring transparency in the use of information, and avoiding manipulation or bias in data interpretation.

In an era of increasing use of artificial intelligence (AI) in education, data literacy is becoming a key competency. AI-based systems analyse large amounts of data and generate recommendations about student progress, engagement, or performance. For such information to be useful and credible, teachers and students need to understand how these systems arrive at their results, what methodological assumptions they make, and what their limitations are. Critical reflection on how artificial intelligence collects and processes data is essential for preserving the integrity and fairness of the educational process.

Developing data literacy contributes to an academic culture based on evidence, understanding, and responsibility. In this approach, data is not an end in itself but becomes a tool that supports quality learning, teaching, and research.

In conclusion, being data literate means knowing how to read and understand data, using it to support learning and teaching, and acting responsibly, transparently, and in accordance with the ethical standards of the academic community at every step.

In an educational environment, data-literate teachers and students develop the ability to think critically and use data responsibly throughout the educational process.