Designing for uncertain futures: the transformation of knowledge with an algorithmic thinking system in early design studios

Purpose Early design studios operate under conditions of uncertainty where initial parameters do not impose a single solution direction. This study aims to examine how knowledge is acquired, processed and transformed in early design studios through an algorithmic thinking framework, revealing how design can be conceptualized as a traceable and structurally reproducible process in response to open-ended problems. Design/methodology/approach This study examines design processes that develop under uncertainty in early design studios using a qualitative, process-oriented approach. Studios conducted over three periods within the scope of Gazi University Architectural Project II were observed; a different initial information set (actor, place and function) was defined for each period. The processes of six students selected from these three periods were analyzed through sketches, intermediate products, concept roadmaps and outcome/evaluation matrices; the processes were interpreted using an algorithmic thinking model within the framework of information acquisition, processing and transformation stages. Rather than aiming at statistical comparison, the study seeks analytical generalization by identifying recurring patterns of knowledge transformation across different initial conditions. Findings The analysis indicates that, within the scope of the selected cases, common structural characteristics emerge in the algorithmic thinking system despite different initial information sets. The concept roadmaps and outcome matrices reveal comparable organizational logics in the stages of knowledge acquisition, processing and transformation, suggesting that design under uncertainty follows identifiable structural principles rather than a purely random progression. Originality/value This study contributes to the literature by addressing algorithmic thinking as a cognitive and organizational framework for reading design processes. The proposed model offers a structured analytical tool that makes the transformation of information under uncertainty visible and discussable in early design studios.