Time identification of design knowledge push based on cognitive load measurement

Nie, Yafei; Tong, Shurong; Li, Jing; Zhang, Yicha; Zheng, Chen; Fan, Bin

doi:10.1016/j.aei.2022.101783

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…However, it is a complex cognitive skill for designers to purposefully use ML to enhance the UX of products (Clay et al , 2021). To put it more specifically, the UX value creation of ML-enhanced products requires the use of multidisciplinary design knowledge (Nie et al , 2022), and designers need to consider not only the unique characteristics of ML but also numerous complex factors such as the properties of stakeholders and context, which brings cognitive load to designers.…”

Section: Introductionmentioning

confidence: 99%

Towards the conceptual design of ML-enhanced products: the UX value framework and the CoMLUX design process

Sun

Zhou

et al. 2023

AIEDAM

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With the increasing utilization of machine learning (ML) to enhance products’ capabilities, the design research community has begun to explore how to support the conceptual design of ML-enhanced products. However, UX value creation of ML-enhanced products is still challenging because of ML's unique characteristics and numerous complex factors in conceptual design. To help designers create UX value for ML-enhanced products, we developed the UX value framework and the CoMLUX design process. The proposed framework describes how ML, stakeholders, and context co-create the UX value of ML-enhanced products, and identifies the growability and opacity of ML, helping designers systematically understand the co-creators while avoiding cognitive overload. The CoMLUX design process provides practical guidance for designing ML-enhanced products with growability and transparency. At last, we demonstrate the usage methods of the framework and process in an actual project and summarize the inspirations and limitations of our work.

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Section: Introductionmentioning

confidence: 99%