User Experience Design Using Machine Learning: A Systematic Review

Abstract

User experience (UX) is the key to increased productivity by enhancing the usability and interactivity of the product. Machine learning (ML) solutions have raised user and academic awareness of technical innovation. As a result, ML is becoming increasingly popular to improve the quality of UX. Several investigations have highlighted a potential lack of studies on the overall challenges and recommendations for UX using ML. Therefore, more attention should be paid to ML's existence and potential applications across various applications to get the most out of ML techniques to improve the UX design process. To this objective, a systematic review of the literature was performed as to determine the challenges faced by UX designers when incorporating ML in their design process. Recommendations that help UX designers incorporate ML into UX design will be highlighted. Furthermore, the PRISMA approach is used (a process that has been established in the literature), to restrict the chance of bias at the selection stage. Relevant articles in the following four databases were searched: IEEE Xplore, Scopus, Web of Science, and ACM. The findings revealed that the number of publications on issues linked to UX with ML had advanced exponentially. This review highlights the challenges, recommendations, tools, algorithms, techniques and datasets used in different studies. In addition, suggestions are given for future investigations.

Predicting the Students Performance Using WUM and Machine Learning Technique

“…Limited understanding in design practice and incomplete and early-stage research. In this paper [6] the proposed system aims to prioritize User Experience (UX) as a central element in any business solution, including web applications, websites, and mobile apps. It acknowledges that a good solution not only addresses the exact problem but also ensures that end users find it user-friendly.…”

The Application of Deep Learning for the Evaluation of User Interfaces

“…One of the most coveted and valuable applications of ML in UX design is its ability to provide users with a new level of personalization [ 20 ]. ML algorithms that learn from usability data sources can improve the user experience [ 21 ], such as by implementing and testing a system for designing creative web elements using an interactive genetic algorithm in which voting-based feedback from the learning mechanism enables the system to adopt quality measures for visual aesthetics [ 22 ].…”

“…ML algorithms that learn from usability data sources can improve the user experience [ 21 ], such as by implementing and testing a system for designing creative web elements using an interactive genetic algorithm in which voting-based feedback from the learning mechanism enables the system to adopt quality measures for visual aesthetics [ 22 ]. One systematic review of the literature that was conducted to identify the challenges UX designers face when incorporating ML into their design process contains recommendations based on its findings [ 20 ]. In one study, ML-design tools based on UX could use formal models to optimize graphical user interface layouts to meet objective performance criteria [ 23 ], while another used ML to automatically vectorize existing digital GUI designs (using computer vision) to quickly apply them to new projects [ 24 ].…”

Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

“…Given the vast number of applications available in online stores, one of the most crucial attributes to enhance both software quality and user experience is usability [1], [2], [3], [4]. Usability not only contributes to improving the competitiveness of software companies but also enhances user productivity in interaction [5], [6], [7], [8].…”