Machine Learning-Assisted Tools for User eXperience Evaluation: A Systematic Mapping Study

Context: Information Systems (IS) have grown exponentially, significantly influencing professional and personal environments. Both scenarios require a distinguished User Experience (UX), which generates positive feelings such as loyalty, learning, and satisfaction from end users. Consequently, tools, software, and applications that integrate Machine Learning (ML) techniques with UX are necessary for enhancing the quality of IS and increasing the productivity of UX specialists. Problem: There is a continued need for more experimental evidence regarding the development, employability/ applicability, evaluation, and evolution of current technologies that automate manual tasks performed by experts. Specifically, such technologies aim to reduce workload, eliminate evaluation biases, and identify patterns that might go unnoticed during assessments. Method: This work aims to summarize and characterize, through a Systematic Mapping Study (SMS), the tools that employ ML techniques to assist in the UX evaluation process. To help us, we defined seven sub-questions that will be addressed based on the data collected from the selected studies. Contributions and Impact: Based on the selected studies, we analyzed and characterized the assessment tools to provide a comprehensive understanding for both the academic and professional communities. This work presents the current state of tools that integrate ML techniques for UX evaluation, offering valuable insights into their effectiveness and application within the IS domain.