Machine learning-assisted tools for user experience evaluation: a systematic mapping study - (:unkn) unknown (Universidade Federal do Pampa, 2025)
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 evelopment, 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. IS Theory: Not applicable. Results Summary: We identified 24 relevant studies between tools and tool proposals. The analysis revealed that these tools are predominantly developed for mobile platforms, with support for web and desktop applications. We realized that tools focus on application sentiment analysis. Contributions and Impact on the IS Area: 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.
Keywords: Mapeamento Sistemático da Literatura; User Experience; Machine Learning; CIENCIAS EXATAS E DA TERRA