Data Visualisation in AI-assisted Decision-making: A Systematic Review

Giulia Neri¹Shevyn Marshall¹Harry Kai-Ho Chan^1*Abdallah Yaghi¹Dash Tabor²Rahul Sinha²Suvodeep Mazumdar¹

• ¹The University of Sheffield, Sheffield, United Kingdom
• ²Tubr, Sheffield, United Kingdom

This study examines the utilisation, challenges, and design principles of data visualisation approaches, focussing on their applications within decision-making contexts. Through a systematic literature review (SLR), this research synthesises insights from existing visualisation approaches and evaluates key aspects such as usability, interactivity, accessibility, and cognitive load management. The identified challenges include achieving a balance between complexity and usability, fostering intuitive design, and providing sufficient training to aid accurate interpretation of complex data. Specific visual elements, such as colour usage, symbolic representation, and data density control, are highlighted as essential for enhancing user comprehension and supporting effective decision-making. This study further underscores the importance of diverse evaluation methods, including usability testing, surveys, and cognitive assessments, to iteratively refine visualisation approaches based on user feedback. Our findings suggest that users benefit most from customisable, interactive approaches that cater to varied cognitive preferences and incorporate continuous training to reduce interpretive biases. This research contributes to best practice development for designing accessible, effective visualisation approaches suited to the complex needs in data-centric environments.