<p>Eye-tracking measures, which provide crucial insight into the processes underlying human language cognition, perception, and social behavior, are particularly important in research with preverbal infants. Until recently, infant eye-gaze analysis required either expensive corneal-reflection eye-tracking technology or labor-intensive manual annotation (coding). Fortunately, iCatcher+, a recently developed AI-based automated gaze annotation tool, promises to reduce these expenses. To adopt this tool as a mainstream tool for gaze annotation, it is key to determine how annotations produced by iCatcher+ compare to the annotations produced by trained human coders. Here, we provide such a comparison, using 288 videos from a word-learning experiment with 12-month-olds. We evaluate the agreement between these two annotation systems and the effects identified using each system. We find that (1) agreement between human-coded and iCatcher+-annotated video data is excellent (88%) and comparable to intercoder agreement among human coders (90%), and (2) both annotation systems yield the same patterns of effects. This provides strong assurances that iCatcher+ is a viable alternative to manual annotation of infant gaze, one that holds promise for increasing efficiency, reducing the costs, and broadening the empirical base in infant eye-tracking.</p>