Abstract
In recent years, several kinds of machine learning tools have developed, each involving complex functions and tasks, which means usage knowledge varies between tools. Integrating the environment for effective AI machine learning can be regarded as a complicated task and may even consist of several separate tasks, such as building a test environment, data acquisition, data cleansing, machine learning training, and model management. In terms of the cognitive engineering approach, most tasks not only require knowledge-based cognitive control over skill-based or rule-based behaviours higher cognitive loads and workloads as well. Since complex knowledge and higher cognitive loads are required, the use of AI machine learning is limited and leads to ineffective work procedures. Thus, this research analysed the AI development process via various methods of cognitive task analysis in order to identify which tasks induce cognitive workload. Then, a new integrated AI development system was created, which was expected to reduce the number of ineffective tasks and workload. Experiments were conducted twice to validate the system’s effectiveness, and the results indicate that there were significant differences between the several different AI development tasks.
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Park, D., Park, H., Song, S. (2020). Designing the AI Developing System Through Ecological Interface Design. In: Ahram, T., Falcão, C. (eds) Advances in Usability, User Experience, Wearable and Assistive Technology. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1217. Springer, Cham. https://doi.org/10.1007/978-3-030-51828-8_12
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DOI: https://doi.org/10.1007/978-3-030-51828-8_12
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