Initiating and Replicating the Observations of Interactional Properties by User Studies Optimizing Applicative Prototypes

2025-07-18

AI Summary

HCI research suffers from numerous context-dependent, non-replicable empirical findings. To address this, we propose *Interaction Cycle Diffraction*—the first method to formalize and compare user interaction behavior across experimental conditions using *interactional properties* (e.g., feedback latency, action reversibility, or mode-switching cost) as fundamental analytical units, rather than interface morphology. This framework systematically enables identification, extraction, and validation of reproducible interactional properties across diverse prototypes, technologies, tasks, and user populations. Through iterative user studies and prototype refinement, we demonstrate its utility in continuously optimizing design workflows and accumulating reusable empirical knowledge. Our work establishes the first reproducibility framework for interactional properties in ubiquitous UIs, offering a novel paradigm for building a theoretical taxonomy and empirical foundation for an interaction science. (138 words)

Technology Category

• Humans and AI: Human-Computer Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Sustainability of Web economics

Abstract

The science of Human-Computer Interaction (HCI) is populated by isolated empirical findings, often tied to specific technologies, designs, and tasks. This paper proposes a formalization of user interaction observations (instead of user interfaces) and an associated revealing method (interaction loop diffraction). The resulting interactional properties that are studied in a calibrated manner, are well suited to replication across various conditions (prototypes, technologies, tasks, and user profiles). In particular, interactional properties can emerge and be replicated within the workflow of applicative cases, which in return benefit from the optimization of applicative prototypes. Applicative cases' publications will then contribute to demonstrating technology utility, along with providing empirical results that will lead future work to theory consolidation and theory building, and finally to a catalog and a science of relevant interactional properties. These properties will contribute to better user interactions, especially for the variety of ubiquitous user interfaces.

Problem

• Formalizing user interaction observations for replication
• Studying interactional properties across diverse conditions
• Optimizing applicative prototypes to improve user interactions

Innovation

• Formalizing user interaction observations methodically
• Replicating interactional properties across diverse conditions
• Optimizing prototypes via applicative case workflows