This article presents a 3-D medical simulation that employs reinforcement learning (RL) and interactive RL (IRL) to teach and assess the procedure of donning and doffing personal protective equipment (PPE). The simulation is motivated by the need for effective, safe, and remote training techniques in medicine, particularly in light of the COVID-19 pandemic. The simulation has two modes: a tutorial mode and an assessment mode. In the tutorial mode, a computer-based, ill-trained RL agent utilizes RL to learn the correct sequence of donning the PPE by trial and error. This allows students to experience many outlier cases they might not encounter in an in-class educational model. In the assessment mode, an IRL-based method is used to evaluate how effective the participant is at correcting the mistakes performed by the RL agent. Each time the RL agent interacts with the environment and performs an action, the participants provide positive or negative feedback regarding the action taken. Following the assessment, participants receive a score based on the accuracy of their feedback and the time taken for the RL agent to learn the correct sequence. An experiment was conducted using two groups, each consisting of ten participants. The first group received RL-assisted training for donning PPE, followed by an IRL-based assessment. Meanwhile, the second group observed a video featuring the RL agent demonstrating only the correct donning order without outlier cases, replicating traditional training, before undergoing the same assessment as the first group. Results showed that RL-assisted training with many outlier cases was more effective than traditional training with only regular cases. Moreover, combining RL with IRL significantly enhanced the participants' performance. Notably, 90% of the participants finished the assessment with perfect scores within three iterations. In contrast, only 10% of those who did not engage in RL-assisted training finished the assessment with a perfect score, highlighting the substantial impact of RL and IRL integration on participants' overall achievement.