###### Abstract
In pursuit of optimal performance and a deeper understanding of surgical skill acquisition, recent efforts have implemented deep learning models. [PERSON] et al. successfully trained an artificial neural network using input data from the Sim-Ortho VR simulator to classify participants into three distinct categories. Despite the promising results, the VR simulator allows only for one-handed maneuvers, thus disregarding important information about bimanual dexterity [20]. Moreover, [PERSON] et al. utilized output data from a robotic-assisted surgery VR simulator to develop an ensemble deep neural network (DNN) for predicting the number of attempts and training time required to attain proficiency. The ensemble DNN demonstrated superior performance compared to other ML algorithms, except in cases where Random Forest and Gradient-Boosted Regression Tree algorithms achieved comparable scores [21]. In the aforementioned studies, several limitations can be identified. When utilizing box trainers, external sensors must be attached to the instruments to collect kinematic data. However, these sensors are not always available and can restrict surgeons' hand movements. For VR simulators, access to the API kinematic acquisition module is essential to collect kinematic data, but this is not always provided by the owning company. Additionally, interpreting the recorded data by VR simulators poses challenges for both students and instructors, prompting the need to process performance metrics to extract meaningful features. Furthermore, VR simulators lack the ability to provide students with a clear understanding of their training progress, as they do not indicate the number of remaining trials needed to successfully complete a training course and advance to the next level. Moreover, another limitation relates to the ground-truth labeling method. The majority of skill assessment studies are based on self-proclaimed skills [22] and skill-levels defined by arbitrary thresholds on performance scores evaluated by experts [23]. Some other studies aim to classify junior vs. senior residents, but the exposure to laparoscopy training is not uniform to all the participants within each group [24]. In contrast to the previous studies where trainees are classified into different skill levels (e.g., novices, intermediates, and experts), in this work, we considered an alternative approach. The developed framework provides students with valuable information during their training on the VR simulator about their progress and the current skill level achieved. Similar studies have also highlighted the significance of intra-training feedback since it provides immediate information to the trainee about the skill level achieved as well as the improvements needed to successfully complete the training curriculum [25]. The developed framework involves three different experiments. The first experiment involved the use of a binary classification model to classify students at the Beginning vs.  
End of laparoscopic training. The second experiment performed a three-class classification among students at the Beginning vs. Middle vs. End of training. Lastly, the third experiment involved the development of a regression model that aimed to predict the remaining number of trials required to complete training on a laparoscopic task at a proficient level. Using these models, our research aims to make a valuable contribution to the domain of surgical education and training. We aspire to offer a comprehensive framework for evaluating and guiding the progression of medical students while they acquire key laparoscopic skills. These models have the potential to improve training methodologies, provide personalized learning paths, and elevate the overall quality of surgical education.