Artificial intelligence (AI) has been recognised as a promising technology for methodological progress and theoretical advancement in learning sciences. However, there remains few empirical investigations into how AI could be applied in learning sciences research. This study aims to utilize AI facial recognition to inform the learning regulation behaviors in synchronous online collaborative learning environments. By studying groups of university students (N = 36) who participated in their online classes under the COVID-19 social distancing mandates, we strive to understand the interrelation between individual affective states and their collaborative group members. Theoretically underpinned by the socially shared regulation of learning framework, our research features a cutting-edge insight into how learners socially shared regulation in group-based tasks. Findings accentuate fundamental added values of AI application in education, whilst indicating further interesting patterns about student self-regulation in the collaborative learning environment. Implications drawn from the study hold strong potential to provide theoretical and practical contributions to the exploration of AI supportive roles in designing and personalizing learning needs, as well as fathom the motion and multiplicity of collaborative learning modes in higher education.