Eliciting Expert Knowledge to Inform Training Design
Eliciting Expert Knowledge to Inform Training Design
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汲取专家知识为培训设计提供信息
DOI：
10.1145/3335082.3335091
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Clewley N
中科院分区：
文献类型：
--
作者：
Clewley N
目的确定最适合于在复杂和动态的军事操作环境(例如爆炸物处理)中发现和捕获专家操作员的反思性认知判断的启发式方法，以便制定支持领域新手训练的反思性可解释人工智能(XAI)代理的规范。对候选方法进行了系统和严格的审查，以确定在高风险情况下寻求可行的缓解威胁战略的过程中发现专家情况认识和元认知评价的最有前景的方法。研究成果被综合到访谈协议中，以引出和了解高风险、复杂操作环境中专家的现场行动和决定。实际影响进入高风险操作环境的培训人员在学习该行业的同时，可以从接触专家反思策略中受益。典型的操作员培训侧重于威胁缓解的技术方面，但往往忽略了反思的自我评估。本研究是朝着确定设计反射性XAI试剂以提高进入高风险操作的受训者的表现的可行性迈出的第一步。此处记录的专家知识获取协议的输出应用于提炼专家操作员判断的理论框架，以确定对领域新手有利的决策支持策略。
PurposeTo determine the elicitation methodologies best placed to uncover and capture the expert operator’s reflective cognitive judgements in complex and dynamic military operating environments (e.g., explosive ordinance disposal) in order to develop the specification for a reflective eXplainable Artificial Intelligence (XAI) agent to support the training of domain novices.ApproachA bounded literature review of the latest developments in expert knowledge elicitation was undertaken to determine the ’art-of-the-possible’ in respects to uncovering an expert’s cognitive judgements in complex and dynamic environments. Candidate methodologies were systematically and critically reviewed in order to identify the most promising methodologies for uncovering expert situational awareness and metacognitive evaluations in pursuit of actionable threat mitigation strategies in high-risk contexts. Research outputs are synthesized into an interview protocol for eliciting and understanding the in-situ actions and decisions of experts in high-risk, complex operating environments.Practical implicationsTrainees entering high-risk operating environments can benefit from exposure to expert reflective strategies whilst learning the trade. Typical operator training focuses on technical aspects of threat mitigation but often overlooks reflective self-evaluation. The present study represents an initial step towards determining the feasibility of designing a reflective XAI agent to augment the performance of trainees entering high-risk operations. Outputs of the expert knowledge elicitation protocol documented here shall be used to refine a theoretical framework of expert operator judgement, in order to determine decision support strategies of benefit to domain novices.
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