Abstract
In this paper we present our experience in the design, modelling, implementation and evaluation of a conversational medical school tutor (MST), employing AI on the cloud. MST combines case-based tutoring with competency based curriculum review, using a natural language interface to enable an adaptive and rich learning experience. It is designed both to engage and tutor medical students through Digital Virtual Patient (DVP) interactions built around clinical reasoning activities and their application of foundational knowledge. DVPs in MST are realistic clinical cases authored by subject matter experts in natural language text. The context of each clinical case is modelled as a set of complex concepts with their associated attributes and synonyms using the UMLS ontology. The MST conversational engine understands the intent of the user’s natural language inputs by training Watson Assistant service and drives a meaningful dialogue relevant to the clinical case under investigation. The curriculum content is analysed using NLP techniques and represented as a related and cohesive graph with concepts as its nodes. The runtime application is modelled as a dynamic and adaptive flow between the case and student characteristics. We describe in detail the various challenges encountered in the design and implementation of this intelligent tutor and also present evaluation of the tutor through two field trials with third and fourth year students comprising of 90 medical students.
A. Shah—Work done while working at IBM.
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Afzal, S. et al. (2020). AI Medical School Tutor: Modelling and Implementation. In: Michalowski, M., Moskovitch, R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science(), vol 12299. Springer, Cham. https://doi.org/10.1007/978-3-030-59137-3_13
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