Machine learning
Also used: ML
Machine learning is a way of building software that finds patterns in data and uses those patterns to make a prediction, recommendation, or classification.
Why it matters to educators
Machine learning is behind many familiar education tools, from recommendations and transcription to early-warning systems. Knowing its basics helps educators ask better questions about accuracy, fairness, and data use.
The foundation
Instead of writing a rule for every possible situation, developers train a model using examples. The model learns patterns that may help it make a new prediction, but its output depends on the quality, relevance, and limits of the data it learned from.
Machine learning is broader than generative AI. Some models sort, score, or predict rather than write or create. A useful prediction can still be wrong or unfair, especially when past data reflects unequal opportunities or incomplete records.
What this can look like in education
A recommendation system
A learning platform suggests practice activities based on prior responses; the teacher checks whether the recommendations fit the learner rather than treating them as a final placement decision.
Spot patterns in attendance
A school reviews a model's attendance-risk flag alongside current information and supportive conversations, never as an automatic judgment about a student.