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

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.

Applications of artificial intelligence and cognitive science in design

“…This thematic collection encompasses a wide range of studies exploring various aspects of applying AI techniques and cognitive science in design. Some studies have focused on how AI can be used to augment and support humans in design (Sun et al, 2023a;Wang et al, 2023), some explored new user cognitive models for design (Tehranchi et al, 2023), while others probed human design cognition using neurophysiological and biometric measures (Hu et al, 2023;Sun et al, 2023b;Yin et al, 2023;Zhang et al, 2023). Additional studies have exploited AI for analyzing design cognitive data (Chiu et al, 2023;Hu et al, 2023;Sun et al, 2023b;Zhang et al, 2023).…”

“…In this thematic collection, Wang et al (2023) assessed the creativity of AI-generated designs by using both human experts and computational techniques, Tehranchi et al (2023) showed the importance of human behavior predictions in computational models for design, and Sun et al (2023a) indicated the inclusion of human designers in ML lifecycles could better support the generation of design solutions. These three studies have shown the significance and potential necessity of integrating human expertise and intelligence into AI design process.…”

“…The study shows the potential of using NLP techniques to capture highly complicated patterns of thoughts that are iterative twisting and turning. Sun et al (2023a) proposed a framework and a design process for helping designers create user experience (UX) values for ML-enhanced products. ML-enhanced products refer to products that have employed ML to solve complex tasks like humans, such as smart speakers, while UX values refer to the perceptions, emotions, behaviors, physical, and psychological responses offered by a product.…”