Towards the conceptual design of ML-enhanced products: the UX value framework and the CoMLUX design process. (30th March 2023)

• Record Type: Journal Article
• Authors: Sun, Lingyun; Li, Zhuoshu; Zhou, Zhibin; Lou, Shanghua; Li, Wenan; Zhang, Yuyang
• Is Part Of: AI EDAM. Volume 37(2023)
• Publication Date: 2023-03-30
• Subjects: Artificial intelligence -- design methods -- machine learning -- toolkit; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060422000270
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: 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.