Adaptive learning refers to learning programmes that continuously adjust to a learner's knowledge, pace and mistakes. Software analyses answers and then selects suitable exercises, explanations or revision. It is used in schools, universities and corporate training – for instance, when a platform automatically offers an easier intermediate step after a task was answered incorrectly. In machine learning, the term also describes models that keep adapting to new data.
AI-based student feedback refers to comments on essays, assignments or projects generated by an AI system against defined criteria. Learners receive pointers on strengths, structure, argument or language more quickly, and teachers are relieved of repetitive marking. Assessment should be transparent and criteria-based; grades and decisions about performance remain the responsibility of teachers. Example: a draft essay receives suggestions for improvement before submission.
In machine learning, a learning curve is a chart showing a model's performance or error over training time or the amount of training data, usually separately for training and validation data. Its shape reveals whether a model is underfitting or overfitting and whether more data is likely to help. If the validation error stays well above the training error, for example, this points to overfitting. In everyday language, the term also describes how quickly people master a new technology.
Machine learning is a branch of artificial intelligence in which computers derive patterns from data instead of having every rule explicitly programmed. Depending on the task, models learn from labelled examples (supervised), from unlabelled data (unsupervised) or through rewards (reinforcement). Applications range from spam filters and demand forecasts to product recommendations. Quality depends heavily on the quantity and quality of the training data.
