A systematic literature review on UX and usability assessment methods used in 2020s for emerging technologies
Sinthya, Faika Ferdous (2025)
Diplomityö
2025
School of Engineering Science, Tietotekniikka
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025061367632
https://urn.fi/URN:NBN:fi-fe2025061367632
Tiivistelmä
This thesis presents a systematic literature review based on peer-reviewed publications from 2020 to 2024 on usability and user experience (UX) assessment methods applied to emerging technologies such as artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and extended reality (XR). The thesis identifies key evaluation methods, examines their effectiveness, and highlights the methodological innovations that were developed to address rising challenges from increasingly adaptive, immersive, and complex challenges.
The review indicates a transition from traditional evaluation tools towards hybrid, multimodal, and context-aware approaches. Techniques that combine user self-reports with biometric data, real-time analytics, and scenario-based testing have improved the ability to measure users’ cognitive load, emotional engagement, and system trust. Customized methods for AI and immersive systems emphasize algorithm transparency, user control, and real-world relevance.
Despite these advancements, the review reveals consistent challenges, including the lack of a standardized framework, high implementation costs, and limited user inclusivity. The thesis determines that future usability and UX assessments must integrate flexible, scalable, and user-centric approaches to evaluate iterative technologies effectively. These insights contribute to ongoing research and practice in human-computer interaction.
The review indicates a transition from traditional evaluation tools towards hybrid, multimodal, and context-aware approaches. Techniques that combine user self-reports with biometric data, real-time analytics, and scenario-based testing have improved the ability to measure users’ cognitive load, emotional engagement, and system trust. Customized methods for AI and immersive systems emphasize algorithm transparency, user control, and real-world relevance.
Despite these advancements, the review reveals consistent challenges, including the lack of a standardized framework, high implementation costs, and limited user inclusivity. The thesis determines that future usability and UX assessments must integrate flexible, scalable, and user-centric approaches to evaluate iterative technologies effectively. These insights contribute to ongoing research and practice in human-computer interaction.