Multi-source feedback integration boosts design confidence and flexibility
Integrating Multi-Source Feedback in Computational Design
Human-Computer Interaction
Summary
Designers often get feedback from many places like experts, experiments, and computer models, but it’s hard to combine all that information easily. The authors show a way to bring together different types of feedback in one tool called MUSE that doesn’t need coding skills. This tool helps designers see when feedback disagrees, lets them adjust how much they trust each source, and use feedback at different speeds. People using MUSE felt more sure about their designs and could work more flexibly. This method helps connect advanced computer analysis with how designers actually work.
What this means in practice
- •For product designers: Combine expert opinions and simulation feedback easily in one tool to improve product design decisions without programming.
- •For user experience teams: Use multi-source feedback integration to balance user testing results with automated analysis and expert reviews for interface design.
Authors
Francisco Erivaldo Fernandes Junior, Thomas Langerak, Mira Keränen, Danqing Shi, Ardak Alipova, Antti Oulasvirta
Abstract
In real-world design practice, evaluations rarely rely on a single source of judgment. Designers routinely combine expert opinions, empirical studies, and computational models, each with distinct strengths and limitations. While machine learning offers methods to integrate multiple feedback sources, these approaches remain largely inaccessible to designers without technical expertise. In this paper, we explore how to integrate multiple feedback sources, primarily through: (1) a practical approach for multi-source integration, and (2) its implementation in MUSE, a no-code tool that allows designers to combine and balance diverse sources. Our technical findings show that independent modeling of multiple evaluation sources enables exploration across heterogeneous feedback, accommodates different evaluation speeds, surfaces disagreements between sources, and supports an adaptable evaluation setup that designers can reconfigure during their process. In a visualization design study, participants navigated their own judgments alongside simulator feedback, reporting a perception of enhanced confidence and flexibility. Our results highlight the viability of multi-source integration to support computational design, offering a step toward bridging the gap between advanced optimization methods and design practice.