Basic concepts of data literacy

Developing data literacy involves not only understanding how data is collected, processed, and analysed, but also recognising the ethical responsibilities that arise from its use. In today’s educational environment, where artificial intelligence-based systems are increasingly implemented, ethics and privacy protection have become essential components of professional practice for both teachers and students. The way we manage data reflects not only technical expertise but also the core values of education – trust, responsibility, and respect.

Main ethical principles:

• Transparency: Participants in the educational process must clearly understand why, how, and for what purpose their data is collected and processed. It is especially important to ensure that processes involving artificial intelligence algorithms are understandable and can be explained, to prevent invisible automation of decisions that may affect students.
• Informed Consent: Before data is collected, students should be fully informed about the goals and methods of data use, including those applied in analytical artificial intelligence systems. Consent must be freely given, informed, and revocable at any time without negative consequences.
• Data Minimization: Only data necessary to achieve clearly defined educational objectives should be collected. This eliminates unnecessary collection of sensitive information that is not directly relevant to teaching and learning. This principle supports the responsible and purposeful use of information in education.
• Data Security and Protection: Technical and organisational measures must be in place to protect data from unauthorised access, loss, or misuse. This is especially important when working with artificial intelligence systems that rely on large volumes of data and complex processing models. Security infrastructure should include regular testing, encryption, and systems for monitoring potential threats.
• Anonymisation and Pseudonymisation: Whenever possible, personal data should be anonymised or pseudonymised to protect individual privacy and reduce the risk of identification.
  • Anonymisation includes permanently removing all identifying information so that individuals can no longer be identified.
  • Pseudonymisation replaces personal data with coded labels, so re-association with a person is only possible with additional information that is stored separately and securely.

Application in the educational environment:

Protecting the privacy of students and teachers must be a priority at all stages of educational data management – from collection to analysis and application of results. The introduction of artificial intelligence into education adds complexity to data management, as these systems learn from large amounts of information about students and teachers. Therefore, establishing clear policies on data collection, storage, and use is essential for maintaining high ethical standards and protecting the privacy of all those involved.

Reflective questions:

• In your educational environment, how do you strive to align the use of data and artificial intelligence technologies with ethical principles and the protection of student privacy and security?
• How challenging is this process for you when introducing new digital tools and approaches to educational processes?
• What steps could you take to make the use of artificial intelligence in teaching more accountable, transparent, and focused on student well-being?