Description: Just-in-time learning refers to acquiring knowledge as needed, which can be applied in various technological contexts for adaptive learning. This approach allows systems to learn and adapt to new situations in real-time, thereby optimizing their performance and efficiency. Instead of relying on large volumes of pre-stored data, just-in-time learning is based on the ability of systems to process information dynamically and contextually. This is particularly relevant in the broader field of artificial intelligence, where the aim is to enhance learning processes through machine learning techniques. The main characteristic of this type of learning is its flexibility, allowing systems to adjust to changes in the environment or in the tasks they need to perform. Additionally, just-in-time learning promotes personalization, as systems can adapt to the specific needs of users or to changing environmental conditions. In summary, this approach not only enhances the efficiency of learning in various technological applications but also opens new possibilities for artificial intelligence, making systems more autonomous and capable of continuous learning.
