Design and Development of an AI-Enhanced Collaborative Chat Platform for Medical Education

Computer-supported collaborative learning (CSCL) can greatly benefit from adaptive scaffolding, which requires analyzing the contributions of each learner and taking actions to facilitate discussion. In this paper, we present a platform capable of discourse analysis and real-time support with GPT-based Conversational Agents (CA), providing an architecture supporting CA design. A case study within medical education fostering collaborative clinical reasoning about simulated patient cases in the PaFaSi environment is presented to demonstrate how an iterative design process involving subject matter experts can improve the performance of GPT-based CAs. Preliminary results of the pilot study show that the PaFaSi CA provided learners with adequate feedback as well as scaffolding for their collaborative clinical reasoning in most cases.