Computational Precedent-Based Instruction (CPBI): Integrating Precedents and BIM-Based Parametric Modeling in Architectural Design Studio

Abstract

Architectural design education aims to balance creativity and analytical thinking. However, design studios have traditionally emphasized intuitive approaches over systematic processes. This study developed and evaluated a pedagogical model termed Computational Precedent-Based Instruction (CPBI), which integrates precedent-based instruction with BIM-based parametric modeling in the architectural design studio. The research explored CPBI’s impact on students’ design skills, identified perceived benefits and challenges, and assessed its effectiveness in promoting systematic design thinking. The study employed a mixed-methods approach, combining model-based inquiry and quasi-experimental research. It involved 19 third-year undergraduate architecture students in a 14-week design studio course. Data collection utilized pre–post surveys, external experts review of student work, and observational data. The pedagogical intervention focused on developing architectural forms, defining aesthetics, and refining building programs using the works of the New York Five architects as precedents. The results showed statistically significant improvements in students’ self-reported design competencies, particularly in precedent analysis, principle application, and design articulation. A shift towards more structured design reasoning was evident. The CPBI model provides a systematic framework for extracting and applying design knowledge from precedents, bridging the gap between conceptual design thinking and digital tools. It contributes to repositioning BIM as an integral design environment in the early design stages, offering implications for both architectural education and professional practice.