The role of artificial intelligence in identifying student learning needs

Accurately identifying student needs is the foundation of a successful and pedagogically sound educational process.

In higher education, students arrive with varying levels of prior knowledge, motivational profiles, learning strategies and digital competencies. These differences directly affect their ability to follow the material and participate in educational activities. In large and heterogeneous groups, it is often difficult to recognise in a timely manner when a student needs additional support or an adjustment in the teaching approach.

Traditional methods of monitoring progress, such as periodic knowledge checks or teacher observations, usually provide only partial and delayed insight into the learning process. Often, it is only after the first formal assessment that it becomes clear some students have already fallen behind in understanding basic concepts, even though this was not previously apparent.

The development of systems based on artificial intelligence brings significant progress in identifying student needs. By analysing data on behaviour and achievements in digital environments – such as error patterns, task completion time and level of engagement – it is possible to identify which students need additional support and at which learning steps difficulties occur. Based on these insights, systems can suggest individualised adjustments and recommendations for progression.

This lesson focuses on understanding the diverse learning needs of students and the processes and techniques by which they can be identified in digital environments. Emphasis is placed on the advantages and possibilities of applying artificial intelligence, as well as the importance of teachers in making informed and ethically responsible pedagogical decisions that ensure personalised, stimulating and more effective learning.