Data literacy and artificial intelligence – biases and limitations

6. Conclusion

In modern education, the abundance of data and the increasing use of artificial intelligence demand a new level of professional responsibility and understanding from teachers and students. Data literacy is becoming a core competency – it enables critical reflection on how data is collected, analysed, and interpreted, and it increases awareness of potential biases and ethical challenges that may arise from its use.

Developing data literacy also involves understanding how data-driven decisions affect learning, assessment, and lesson planning. Combined with ethical principles and pedagogical sensitivity, it supports the responsible use of artificial intelligence in education – respecting diversity, promoting fairness, and strengthening trust in digital tools and systems.

Ultimately, only data-literate participants in the educational process can make informed decisions and use artificial intelligence technologies in ways that truly improve the quality of learning and teaching, without undermining the fundamental principles.

Reflective activity:

Reflect on your own experience using digital tools and systems that incorporate elements of artificial intelligence in education.

• How do you use student data (or your own learning data) to improve learning outcomes?
• Are you aware of potential biases in the data you analyse or use?
• How could you improve your approach to data literacy to ensure that the use of AI in education is fair, safe, and transparent?

Write down a few concrete examples from your own practice or from your environment.