Learning in groups allows students to develop academic and social competencies but requires the presence of a human teacher that is actively guiding the group. In this paper we combine data-mining and visualization tools to support teachers’ understanding of learners’ activities in an inquiry based learning environment. We use supervised learning to recognize salient states of activity in the group’s work, such as reaching a solution to a problem, exhibiting idleness, or experiencing technical challenges. These “critical” moments are visualized to teachers in real time, allowing them to monitor several groups in parallel and to intervene when necessary to guide the group. We embedded this technology in a new system, called SAGLET, which augments existing collaborative educational software and was evaluated empirically in real classrooms. We show that the recognition capabilities of SAGLET are compatible with that of a human domain expert. Teachers were able to use the system successfully to make intervention decisions in groups when deemed necessary, without overwhelming them with information. Our results demonstrate how AI can be used to augment existing educational environments to support the “teacher in the group”, and to scale up the benefits of group learning to the actual classroom.