How children acquire language in just a few years, from babble to grammar, remains one of the most compelling puzzles in cognitive science. Two challenges have long constrained progress: getting the data, that is, capturing language learning as it happens in children’s rich, messy, naturalistic environments, and making sense of it, via manual annotation that is slow, costly, and impractical at scale. Over the past decade, technological advances have begun to reshape both challenges at once. The transformation has been rapid and wide-ranging, spanning the hardware that captures children’s everyday environments, the algorithms that extract meaning from raw signals, and the scientific questions that can now be addressed as a consequence. In this review, we examine the latest work, published between 2024 and 2026, on ML both as a tool, enabling behavioral measurement at scales previously unattainable, and as a model, allowing researchers to instantiate and test explicit hypotheses about learning mechanisms.
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