In traditional medical education, learners are mostly trained to diagnose and treat patients through supervised practice. Artificial Intelligence and simulation techniques can complement such an educational practice. In this paper, we present GLARE-Edu, an innovative system in which AI knowledge-based methodologies and simulation are exploited to train learners “how to act” on patients based on the evidence-based best practices provided by clinical practice guidelines. GLARE-Edu is being developed by a multi-disciplinary team involving physicians and AI experts, within the AI-LEAP (LEArning Personalization of AI and with AI) Italian project. GLARE-Edu is domain-independent: it supports the acquisition of clinical guidelines and case studies in a computer format. Based on acquired guidelines (and case studies), it provides a series of educational facilities: (i) navigation, to navigate the structured representation of the guidelines provided by GLARE-Edu, (ii) automated simulation, to show learners how a guideline would suggest to act, step-by-step, on a specific case, and (iii) (self)verification, asking learners how they would treat a case, and comparing step-by-step the learner’s proposal with the suggestions of the proper guideline. In this paper, we describe GLARE-Edu architecture and general features, and we demonstrate our approach through a concrete application to the melanoma guideline and we propose a preliminary evaluation.

On the basis of our 25-year experience with the GLARE (Guideline Acquisition, Representation and Execution) clinical decision support system, we have started to analyze the adoption of computer-interpretable clinical guidelines (CIGs) and AI techniques to train and test medical students about how to act on patients. Moving from decision support to the educational task involves significant research challenges. In this paper, we propose a new facility that supports teachers in the definition of tests, by selecting and hiding to students specific parts of the CIG, and asking students how they would act on the given case study (patient) in the selected parts. Students are provided with a medical ontology to identify proper actions/decisions, and students' proposals are then automatically compared with what the CIG (considered as a "golden standard") would suggest to do to the patient through knowledge representation and reasoning techniques. Our basic explanation mechanism exploits the medical ontology to show to students the differences (if any) between their proposals and the ones of the CIG.