Developing a dynamic inference expert system to support individual learning at work

In response to the rapid growth of information in recent decades, knowledge-based systems have become an essential tool for organizational learning. The application of electronic performance-support systems in learning activities has attracted considerable attention from researchers. Nevertheless, the vast, ever-increasing amount of information is creating management problems regarding the efficiency and accuracy of knowledge retrieval. This study aims to develop a dynamic inference expert system ( DIES) to solve these problems. DIES integrates web technology, knowledge management, Extensible Markup Language ( XML) and C Language Integrated Production System ( CLIPS) to facilitate the convenience of knowledge retrieval and the efficiency of learning at work. The advantages of XML include scalability, sharing, easy readability and the ability to enhance the efficient management of knowledge-based systems. In DIES, organizational knowledge is automatically generated as machine-readable language and knowledge description, using CLIPS as a rule-conduction tool to develop specific organizational knowledge bases. A data source consisting of 22 participants from different industries was employed to examine the effectiveness of DIES. Results indicate that participants intend to apply DIES within their industries, with a 95% probability of attaining above-average satisfaction. [ABSTRACT FROM AUTHOR]

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