Active learning (AL) is a central response to the annotation bottleneck in modern artificial intelligence: when labels are expensive, a learner should query for the most useful forms of supervision rather than indiscriminately acquiring labels. However, contemporary AL is no longer a unified field organized around a small set of stable query principles. It is fragmented across acquisition strategies, supervision granularities, operational regimes, and evaluation protocols, making reported gains