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Gamification in the study of anatomy: The use of artificial intelligence to improve learning
Author(s) -
Bracaccio Rafael,
Hojaij Flavio,
Notargiacomo Pollyana
Publication year - 2019
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.2019.33.1_supplement.444.28
Subject(s) - computer science , field (mathematics) , process (computing) , virtual reality , ranking (information retrieval) , multimedia , human–computer interaction , artificial intelligence , mathematics , pure mathematics , operating system
In medicine, the study of anatomy is of utmost importance since this fundamental course is relevant to all medical careers. Every single medical student must learn anatomy regardless of which specialization they choose to follow. Technology‐assisted learning is a growing topic not only in medicine, but in each and every area of study. There is a large number of possible approaches to teach/learn assisted by technology such as educative and serious games, the use of virtual and augmented reality, hardware and software created specifically for training a certain skill and many more. Based on this approach, in this research we developed a gamified mobile application to help both the students and the professor at anatomy classes to understand and develop knowledge in this field based on different kinds of media such as videos, texts, images, audios, quizzes and so on. Gamification consists in the use of game elements in different contexts outside games. This involves presenting aspects such as ranking, badges, quests, etc. to promote engagement and institutes an immersion and flow in faculty education. In order to do that, it was applied the Marczewski's Gamification Design Framework, which involves the definition of the problem, users, success to project and building the user journey to attain some specific behaviours, actions and emotion which can be refined to complete the process and promote learning. This framework was combined with Artificial Intelligence techniques to recognize the difficulties of each individual student and is then able to provide targeted content based on the student performance. Support or Funding Information The authors thank Mackpesquisa for the financial support. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .