Domain Atlas

Education AI

AI in education spans early-warning prediction, remote-exam proctoring, tutoring assistants, and automated grading, and its subjects are students — often minors — so the deploying institution's duty of care and the training of the staff who read the AI's output are load-bearing. Two deployment patterns anchor the governance. In predictive early warning, a model labels a student's risk of not graduating and delivers it to school staff, and the label is only as good as the intervention it triggers and the training of the human who reads it: a statewide dropout early-warning system was found by an independent audit to be wrong most of the time when it predicted a student would not graduate, to produce higher false-alarm rates for Black and Hispanic students, and to reach staff who reported no training on how to interpret a 'high risk' label — so the model's error and its group disparity were imported into how students were seen rather than into help they received, and the system was withdrawn. Against that, a large district's transparent, low-tech on-track indicator, paired with real intervention, accompanied a rise in graduation to a record level — locating the benefit in the intervention the indicator makes legible, not in the sophistication of the prediction. In remote proctoring, surveillance-based AI carries a rights cost that can be independently adjudicated: a public university's requirement that students pan their webcam around their home before an exam was held to be an unreasonable search, and peer-reviewed measurement found proctoring software flags darker-skinned and Black students more often with no corresponding difference in actual cheating. The Lab networks model only the deploying institution — its model or proctoring tool, the staff and proctors who read its output, and its student records; the students being scored or watched sit outside the dynamics, and no student outcome is computed on any diagram.