Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by core deficits in social interaction and communication. Collaborative puzzle games are interactive activities that can be played to foster the collaboration and verbal-communication skills of children with ASD. In this paper, we have designed an intelligent agent that can play collaborative puzzle games with children and verbally communicate with them as if it is another human player. Furthermore, this intelligent agent is also able to automatically measure children's task-performance and verbal-communication behaviors throughout game play. Two preliminary studies were conducted with children with ASD to evaluate the feasibility and performance of the intelligent agent. Results of Study I demonstrated the intelligent agent's ability to play games and communicate with children within the game-playing domain. Results of Study II indicated its potential to measure the communication and collaboration skills of human users.

Recent usage of Virtual Reality (VR) technology in surgical training has emerged because of its cost-effectiveness, time savings, and cognition-based feedback generation. However, the quantitative evaluation of its effectiveness in training is still not studied thoroughly. This paper demonstrates the effectiveness of a VR-based surgical training simulator in laparoscopic surgery and investigates how stochastic modeling represented as Continuous-time Markov-chain (CTMC) can be used to explicit the training status of the surgeon. By comparing the training in real environments and in VR-based training simulators, the authors also explore the validity of the VR simulator in laparoscopic surgery. The study further aids in establishing learning models of surgeons, supporting continuous evaluation of training processes for the derivation of real-time feedback by CTMC-based modeling.

This study presents a 3D medical simulation that employs reinforcement learning (RL) and interactive reinforcement learning (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 10 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.