Abstract :
[en] While computational design tools and practices have been developing at a fast pace, their adoption in architecture remains relatively limited. This paper investigates how users of various profiles engage with pre-existing computational design logic, focusing on whether a lack of familiarity with a given toolset constitutes a significant limitation to its effective use. It examines how context familiarity influences interactions with a parametric façadegeneration script implemented across three distinct interfaces. By combining automated logging of user behaviour with survey-based feedback, the study highlights both an initial "learning phase" and the gradual convergence of behaviour over time-underscoring that once users grasp the underlying computational logic, skill disparities diminish. Rather than emphasizing tool-specific proficiency, the results point to how interface constraints and parameter framing affect exploration and engagement. These findings reveal that well-structured parameters and interface transparency are as critical as prior expertise, suggesting that even novices can successfully engage with Computational Design when provided intuitive, open-ended environments. As a pilot exploration studying behavioural analytics based on automated data logging, this work opens avenues for further research into how interface design, parameter clarity, and user-learning trajectories can foster broader industry uptake of computational methods.
