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

Lingyun Sun, Zhuoshu Li, Zhibin Zhou, Shanghua Lou, Wenan Li, Yuyang Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Article numbere13
JournalArtificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM
Volume37
DOIs
Publication statusPublished - 30 Mar 2023

Keywords

  • Artificial intelligence
  • design methods
  • machine learning
  • toolkit

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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