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Intelligent Systems in Learning and Education

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Intelligent Systems in Medicine and Health

Part of the book series: Cognitive Informatics in Biomedicine and Healthcare ((CIBH))

Abstract

The topic of Intelligent Systems in Learning and Education cuts across many of the issues discussed in several chapters in this book, including explainability, medical image interpretation, clinical decision support, conversational agents and cognitive informatics. Most current digital applications in learning deliver pre-scripted information, with sequencing changes based on user interaction. An intelligent system customizes its content and delivery, in real-time, based on learner performance, errors, misconceptions, needs and affect and based on principles of cognitive and learning sciences. Essential components of any specific intelligent learning system include a representation of the desired end state for the learner, a representation of the current state of the learner’s evolving knowledge of this content, a method of assessing the gap between the current and desired states, and a method to select the next preferred or an optimal chunk of information to progress towards the desired end state. The learner interacts with the intelligent system using a screen mouse or other technology. The system gathers feedback from the learner’s inputs and selections. A conversational intelligent system adds the capability of verbal dialog, with further intelligence applied to systems such as a question generation capability. An intelligent simulation applies intelligence not only to the interaction with the learner but also to interactions between its components. Such intelligence may be the response of the simulated physiology to the learner’s interaction with the simulated pharmaceutical interventions, in the context of resuscitation of a virtual patient during organ failure. Adding gaming to a high-fidelity simulation brings in the potential for competition and collaboration. This chapter begins with a brief history of curricular development in medical education, followed by a description of current work and the potential of AI to enhance the depth and granularity of learning scenarios, emphasizing emerging approaches to improve the training and education of medical practitioners. It will further discuss the relationship of training with the specified outcome to learn and acquire knowledge and skills for adaptive transfer to other clinical situations, using cognitive and learning sciences principles.

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Notes

  1. 1.

    https://www.solaresearch.org/about/what-is-learning-analytics/ (accessed August19, 2022).

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Authors and Affiliations

  1. New York Academy of Medicine, New York, NY, USA

    Vimla L. Patel

  2. SimTabs LLC, Los Altos Hills, CA, USA

    Parvati Dev

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Editors and Affiliations

  1. University of Washington, Seattle, WA, USA

    Trevor A. Cohen

  2. New York Academy of Medicine, New York, NY, USA

    Vimla L. Patel

  3. Columbia University, New York, NY, USA

    Edward H. Shortliffe

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Patel, V.L., Dev, P. (2022). Intelligent Systems in Learning and Education. In: Cohen, T.A., Patel, V.L., Shortliffe, E.H. (eds) Intelligent Systems in Medicine and Health. Cognitive Informatics in Biomedicine and Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-09108-7_16

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