In science laboratories, accidents happen due to human errors, including carelessness and negligence of laboratory guidelines by the students, resulting in personal injuries and damaged assets. The lab supervisor (hereafter, the teacher) sets certain laboratory protocols for the students to follow to ensure the safety of the laboratory's experimental conditions and avoid potential accidents. One of the first rules students need to follow daily is to wear appropriate personal protective equipment, such as aprons, goggles, and gloves, before conducting an experiment. However, it is time-consuming for the teacher to check them daily. Therefore, this process needs to be automated. To address this research issue, we proposed the Laboratory Safety Assistant (LSA) framework, a robot-assisted learning framework to detect students’ behaviors that could lead to accidents and cause personal injuries during experiments. The LSA framework comprises Misty II Plus, a social robot as a social companion, a Behavior Detection and Analysis (BDA) server, and an intervention decision-making dashboard. We prepared a ‘Lab Objects’ dataset for this study containing 1288 images and 2295 objects grouped into 7 classes. We used this dataset to develop a new object detection model trained on a pre-trained YoloV8 model that could analyze image and video data captured by Misty II Plus. In addition, we developed an educational intervention for the teachers who lead science laboratories. Using this intervention, the teacher assesses students’ in-lab behaviors, identify their areas of support, and guide them in addressing the areas of need.