Kabanza Froduald, Bisson Guy, Charneau Annabelle, Jang Taek-Sueng
Department of Computer Science, University of Sherbrooke, Sherbrooke, Que., Canada J1K2R1.
Artif Intell Med. 2006 Sep;38(1):79-96. doi: 10.1016/j.artmed.2006.01.003. Epub 2006 May 2.
This paper describes an approach for developing intelligent tutoring systems (ITS) for teaching clinical reasoning.
Our approach to ITS for clinical reasoning uses a novel hybrid knowledge representation for the pedagogic model, combining finite state machines to model different phases in the diagnostic process, production rules to model triggering conditions for feedback in different phases, temporal logic to express triggering conditions based upon past states of the student's problem solving trace, and finite state machines to model feedback dialogues between the student and TeachMed. The expert model is represented by an influence diagram capturing the relationship between evidence and hypotheses related to a clinical case.
This approach is implemented into TeachMed, a patient simulator we are developing to support clinical reasoning learning for a problem-based learning medical curriculum at our institution; we demonstrate some scenarios of tutoring feedback generated using this approach.
Each of the knowledge representation formalisms that we use has already been proven successful in different applications of artificial intelligence and software engineering, but their integration into a coherent pedagogic model as we propose is unique. The examples we discuss illustrate the effectiveness of this approach, making it promising for the development of complex ITS, not only for clinical reasoning learning, but potentially for other domains as well.
本文描述了一种用于开发临床推理教学智能辅导系统（ITS）的方法。
我们用于临床推理的ITS方法在教学模型中采用了一种新颖的混合知识表示法，结合有限状态机来模拟诊断过程中的不同阶段，生产规则来模拟不同阶段反馈的触发条件，时态逻辑基于学生问题解决轨迹的过去状态来表达触发条件，以及有限状态机来模拟学生与TeachMed之间的反馈对话。专家模型由一个影响图表示，该图捕捉了与临床病例相关的证据和假设之间的关系。
这种方法已在TeachMed中实现，TeachMed是我们正在开发的一个患者模拟器，用于支持我们机构基于问题的学习医学课程的临床推理学习；我们展示了使用这种方法生成的一些辅导反馈场景。
我们使用的每种知识表示形式在人工智能和软件工程的不同应用中都已被证明是成功的，但将它们集成到我们所提出的连贯教学模型中是独一无二的。我们讨论的示例说明了这种方法的有效性，使其不仅在临床推理学习方面，而且在其他领域开发复杂的ITS方面都具有潜力。
