From Abstraction to Realization: A Diagrammatic BIM Framework for Conceptual Design in Architectural Education

Abstract

The conceptual design phase in architecture establishes the foundation for subsequent design decisions and influences up to 80% of a building’s lifecycle environmental impact. While Building Information Modeling (BIM) demonstrates transformative potential for sustainable design, its application during conceptual design remains constrained by perceived technical complexity and limited support for abstract thinking. This research examines how BIM tools can facilitate conceptual design through diagrammatic reasoning, thereby bridging technical capabilities with creative exploration. A mixed-methods approach was employed to develop and validate a Diagrammatic BIM (D-BIM) framework. It integrates diagrammatic reasoning, parametric modeling, and performance evaluation within BIM environments. The framework defines three core relationships—dissection, articulation, and actualization—which enable transitions from abstract concepts to detailed architectural forms in Revit’s modeling environments. Using Richard Meier’s architectural language as a structured test case, a 14-week quasi-experimental study with 19 third-year architecture students assessed the framework’s effectiveness through pre- and post-surveys, observations, and artifact analysis. Statistical analysis revealed significant improvements (p < 0.05) with moderate to large effect sizes across all measures, including systematic design thinking, diagram utilization, and academic self-efficacy. Students demonstrated enhanced design iteration, abstraction-to-realization transitions, and performance-informed decision-making through quantitative and qualitative assessments during early design stages. However, the study’s limitations include a small, single-institution sample, the absence of a control group, a focus on a single architectural language, and the exploratory integration of environmental analysis tools. Findings indicate that the framework repositions BIM as a cognitive design environment that supports creative ideation while integrating structured design logic and performance analysis. The study advances Education for Sustainable Development (ESD) by embedding critical, systems-based, and problem-solving competencies, demonstrating BIM’s role in sustainability-focused early design. This research provides preliminary evidence that conceptual design and BIM are compatible when supported with diagrammatic reasoning, offering a foundation for integrating competency-based digital pedagogy that bridges creative and technical dimensions of architectural design.

1. Introduction

The architecture, engineering, and construction (AEC) industry faces unprecedented challenges in addressing climate change and resource depletion, with the built environment responsible for approximately 40% of global carbon emissions [1]. Early design decisions are particularly critical, as they determine up to 80% of a building’s lifecycle environmental impact [1]. Recognizing this critical impact, Education for Sustainable Development (ESD) has emerged as a transformative approach within architectural education [2,3,4]. ESD equips future designers with the knowledge, skills, and perspectives necessary to address environmental, social, and economic challenges [5,6,7] through two integrated pathways: cultivating essential competencies that align with the Sustainable Development Goals (SDGs) [8,9,10,11] and strategically adopting digital tools during early design stages to support informed, sustainable decision-making [12,13,14,15,16].

Architecture design, at the conceptual stage, depends on abstract thinking, exploration, and creative synthesis—processes that enable designers to establish foundational principles for their projects [17,18,19]. Central to this phase are diagrams, which mediate between abstract concepts and concrete forms, facilitating the transition from intangible ideas to tangible architectural expressions [20,21]. However, current digital tools demonstrate limited capacity to support this critical stage, where critical design decisions are conceived [22,23,24,25].

This limitation is particularly evident with Building Information Modeling (BIM), a transformative AEC technology that holds significant potential for data-driven design decisions [23,24,25]. Despite BIM’s proven capabilities in detailed design phases, its application during conceptual design remains constrained [26,27]. Current BIM tools are perceived as sophisticated yet inadequate for supporting early design exploration. They are criticized for constraining creativity through predetermined building elements while failing to incorporate architectural design language [28,29,30].

This situation presents a fundamental paradox: although the early design phase exerts the greatest influence on project outcomes, it is insufficiently supported by digital tools. Meanwhile, BIM systems—despite their power for data-driven design—are largely inaccessible during this formative stage. Most parametric and digital diagrammatic workflows are tailored to later, detail-oriented design phases or exist as isolated computational exercises. Consequently, a notable gap persists in integrated frameworks that seamlessly link abstract conceptual thinking with BIM’s technical capabilities from the earliest design stages [31].

To address this fundamental disconnect, strategies are needed to reduce BIM complexity, reconceptualize BIM as an architectural knowledge system, and integrate diagrammatic reasoning to support abstract thinking processes [27,31,32,33]. This necessitates educational approaches that demonstrate how digital tools can enhance, rather than constrain, the creative exploration required for innovative and informed design solutions.

This study proposes that integrating parametric, diagrammatic thinking into BIM bridges its technical strengths with the early conceptual design’s exploratory nature. Accordingly, it asks: How can BIM tools support conceptual design by embedding diagrammatic reasoning to facilitate early-stage design exploration?

Anchored in this research question, the study explores the following hypotheses:

H1: 

Students using the D-BIM framework will demonstrate an ability to use BIM for conceptual design processes.

H2: 

Students using the D-BIM framework will demonstrate enhanced diagrammatic reasoning and systematic design thinking abilities.

To test these hypotheses, the study pursues two main objectives:

• Develop and evaluate an integrated diagrammatic BIM framework (D-BIM) that supports conceptual design through diagrammatic reasoning, facilitating fluid exploration and seamless progression to detailed models.
• Assess the impact of the developed diagrammatic reasoning framework on expanding BIM’s role in conceptual architectural design, particularly in educational settings.

This study adopts Richard Meier’s architectural language as a controlled test case, establishing a structured framework for initial validation but also constraining generalizability to other architectural paradigms. The findings offer preliminary evidence that integrating diagrammatic reasoning with BIM can support early design exploration and lay the groundwork for broader BIM integration in formal design studios.

2. Materials and Methods

2.1. Research Design

This study employed a mixed-methods approach combining theoretical framework development with quasi-experimental validation. The research comprised two sequential phases: development of the theoretical foundation and D-BIM framework, followed by validation through a 14-week implementation study with architecture students. Data collection utilized pre–post surveys, structured observations, and artifact analysis.

2.1.1. Development Phase

• Theoretical Framework Development:

The theoretical framework was developed through a critical literature review employing historical–interpretive research methods. This approach, described by Groat and Wang [34], interprets past epistemological evidence to explain current conditions. The review identified the limitations of digital tools, particularly BIM, in supporting conceptual design and explored potential solutions for developing the Diagrammatic BIM Framework (D-BIM).

• Diagrammatic BIM Framework (D-BIM) Development:

The development of D-BIM involved synthesizing literature-based recommendations and solutions to create a system for parametric conceptual diagramming. This framework utilizes three primary tools: Autodesk Revit as the primary modeling environment, Autodesk Forma for environmental analysis, and Enscape for visualization. Richard Meier’s architectural works were selected as the implementation test case, providing a consistent formal language for evaluating the framework’s effectiveness.

Autodesk Revit Architecture was selected as the primary BIM tool due to its massing and computational design features [32]. Autodesk Forma was utilized for environmental analysis due to its capabilities in early-stage design and predictive analytics. Enscape, a plugin for Revit, provided real-time rendering and virtual reality for spatial and qualitative assessment of conceptual designs.

A suitable test case is required to implement and test the D-BIM Framework. Kalay [35] argues that not all design methods are computable due to the reliance of design on intuition and creativity. For computational modeling, the test case must exhibit consistency in rules, vocabularies, and design themes while demonstrating variations in formal expressions. This approach aligns with the concept of formal language as a system of rules governing the composition of design elements [36]. Richard Meier’s work was chosen as the test case because his architecture exhibits a consistent formal language with a defined vocabulary and rules that can be systematically mapped across diverse projects of varying functions, scales, and formal expressions [37,38]. His design process prominently features diagrams that illustrate key relationships such as part-to-whole, circulation, and geometric relationships. Moreover, Meier’s vocabulary aligns with Revit’s predefined generic families, facilitating implementation. Additionally, given the limited resources available for this study, focusing on a single, well-documented architectural language enabled concentrated pedagogical development and effective student guidance within the 14-week timeframe. This approach provided a controlled foundation for implementing and testing the D-BIM Framework while enabling systematic outcome comparisons across participants and promoting collaborative peer learning.

2.1.2. Validation Phase

The validation phase utilized a 14-week single-group pre–post quasi-experimental design to implement and validate the D-BIM framework in an educational setting. This approach aligns with Lendrum and Humphrey’s [39] study of implementation at the effectiveness stage, which links interventions to outcomes in natural educational environments using available resources. The educational setting was chosen for its potential to construct validity that is unattainable in professional contexts [39,40]. Students demonstrate a greater openness to adopting new digital tools than professionals with established preferences, further supporting this choice [41].

Convenience sampling was used to select participants based on accessibility [42]. Participants were recruited through institutional course assignment procedures at the University of Jordan. Students were randomly assigned to course sections by the university’s registration system, with the researcher subsequently assigned as instructor to one Design III section. A total of 19 participants (7 male, 12 female) were enrolled in the Architecture Design III course during the Fall 2024 semester (first semester of the third year). All participants were of similar age (20–21 years) and academic standing. No exclusion criteria were applied, as all enrolled students participated in the standard curriculum. This course was selected due to its pedagogical emphasis on conceptual design development through digital tools, with curricular focus on functional planning, spatial ordering, and form generation logic. Prior to this course, students were prohibited from using digital tools in design studios, relying exclusively on hand-drawing and physical modeling. This course represented their first encounter with digital design tools, ensuring no prior experience with BIM, parametric modeling, or design software, thereby enabling a controlled assessment of the D-BIM framework’s impact without interference from established tool preferences.

Participants implemented the D-BIM framework in designing a 3000 m² public library situated within a moderately complex urban context, requiring the integration of formal, functional, and contextual design constraints.

2.2. Data Collection and Analysis

2.2.1. Survey Instrument and Validation

This study employed quantitative and qualitative data collection techniques to evaluate the efficacy of the D-BIM framework. Pre- and post-intervention surveys utilized five-point Likert scales. The survey assessed students’ perceptions across four key domains: understanding of the design process, utilization of diagrams, impact of BIM tools, and academic self-efficacy (ASE). ASE is defined as an individual’s belief in their capacity to achieve specific academic outcomes [43]. It has been positively correlated with increased effort, persistence, and deeper engagement in learning tasks, which may lead to improved academic performance [44].

2.2.2. Observational Data

The researcher, who also served as the educator, conducted semi-structured observations with field note-taking to provide contextual depth to the quantitative findings [45]. Artifact analysis and evaluation of student work provided tangible evidence of the framework’s effectiveness [46]. To enhance credibility and mitigate researcher bias, an expert in design pedagogy and Meier’s architectural language from the same institution conducted peer debriefing sessions and participated in design critiques at weeks 6, 9, and 14, providing a longitudinal perspective on student progress and comparative insights based on institutional context [47]. To ensure consistent evaluation standards, students used a standardized Revit template for final submissions that focused assessment on design merit rather than presentation quality.

2.2.3. Statistical Analysis

Given the small sample size (n = 19, which is below the threshold of the Central Limit Theorem of n = 30), normality violations compromise the robustness of parametric methods and increase the risk of drawing inaccurate conclusions. The Wilcoxon signed-rank test was selected as this non-parametric approach requires no normality assumptions while maintaining sensitivity to systematic differences in small samples [48]. Hodges–Lehmann median difference and 95% confidence intervals (CIs) were calculated to provide robust estimates of effect magnitude [49]. Effect sizes were calculated using r = Z/√N to provide robust indicators of practical significance regardless of sample size, and interpreted according to Cohen’s conventions (small: 0.1, medium: 0.3, large: 0.5) [50]. Internal consistency reliability was assessed using Cronbach’s alpha for both pre-test and post-test.

2.3. Scope, Limitations, and Ethical Considerations

2.3.1. Study Scope

The research is situated within a third-year architectural studio context, wherein the primary pedagogical emphasis centers on conceptual development facilitated through digital tools such as Building Information Modeling (BIM). The study examines how formal design systems interact with programmatic, spatial, experiential, and contextual factors through diagrammatic reasoning. Environmental analysis tools (solar exposure, wind patterns, daylighting) are introduced as exploratory components rather than comprehensive performance evaluation methods. The study prioritizes pedagogical effectiveness of early-stage BIM integration and diagrammatic reasoning, exploring potential connections to performance analysis without establishing this as a central objective.

2.3.2. Design and Scope Constraints

The study’s educational and exploratory context imposes several limitations:

• Methodological Limitations: The single-group pre–post design without a control group limits causal inference, as observed improvements may be attributed to maturation effects, history effects, or instructor bias rather than the D-BIM intervention [51]. The small sample size (n = 19) from a single institution limits the statistical power and external validity of the findings across diverse educational contexts [52].
• Time Constraints: The 14-week duration limits assessment of long-term learning retention and sustained framework impact.
• Scope Limitations: The focus on Richard Meier’s modernist Western architectural language, while providing methodological control, limits generalizability to approaches rooted in regional cultures, vernacular traditions, or alternative design philosophies. The single project context (public library design) may not represent the framework’s effectiveness across other architectural typologies with different programmatic complexities. Additionally, the Architecture Design III course focuses on formal methods, limiting the full exploration of advanced building technical considerations.

2.3.3. Threats to Validity and Mitigation Strategies

Several threats to internal and external validity were identified and addressed through specific mitigation strategies [40,45,51]:

• Testing Effects: Pre-test exposure may have influenced post-test responses. This was mitigated through 14-week intervals between assessments.
• Hawthorne Effects: Potential observer effects were mitigated by integrating observations into normal studio routines, framing researcher attention as standard pedagogical guidance rather than research evaluation, and conducting longitudinal observation over the full semester to allow behavior normalization.
• Grading Incentives: The integration of the D-BIM framework into regular coursework created potential motivation bias. This was addressed by applying the same grading policy used across all Architecture Design III sections, where assessment was based solely on architectural design quality rather than tool proficiency. Survey participation remained separate from academic assessment.
• Prior Digital Tool Exposure: All participants were third-year students with identical curriculum backgrounds and no previous BIM or parametric modeling experience, minimizing variation in baseline digital competencies.
• Maturation Effects: The 14-week duration created potential for natural skill development independent of the intervention. This was partially mitigated through baseline assessments and triangulation with observational data to distinguish D-BIM-specific improvements from general skill progression.
• Cultural and Institutional Context: The study was conducted within the University of Jordan’s educational culture, which emphasizes structured learning and instructor-guided exploration. These factors may limit external validity to contexts with different pedagogical approaches or curricula where digital skills are introduced earlier.
• Instructor Bias: The researcher’s dual role as instructor and investigator may have influenced outcomes. However, this dual role also fostered ‘prolonged engagement,’ enhancing credibility and reliability through an insider’s perspective [45]. This was further addressed through peer debriefing with an independent expert and the use of structured observations.

Overall, reliability and validity were strengthened through prolonged engagement, data triangulation, peer debriefing, and a comprehensive contextual description of the research process [45].

2.3.4. Ethical Considerations

This research was conducted in accordance with ethical standards for educational research. The study qualified for exempt status under the University of Jordan Institutional Review Board (IRB) guidelines, as it involved evaluation of teaching methods within normal educational settings with minimal risk to participants [53]. Formal IRB approval was not required; however, ethical protocols were maintained throughout the study. Participant protection measures included voluntary participation with explicit right to withdraw, no impact on academic grades or course standing, and verbal informed consent documenting research purpose, procedures, and participant rights. Survey responses were anonymized, and research data remained separate from academic assessment to ensure no coercion. Survey analysis was conducted after semester completion and final grade submission, with grades determined solely by design quality using standardized criteria applied uniformly across all Architecture Design III sections.

3. Theoretical Framework

3.1. Contextual Foundations

Project-based learning (PBL) effectively supports Education for Sustainable Development (ESD) in engineering by enabling students to apply theoretical knowledge to real-world problems [54,55,56,57,58] through iterative design processes [59,60,61] and active learning techniques [62,63]. This approach promotes a comprehensive cycle of conceptualization, experimentation, implementation, and refinement [61].

Within architectural education, preparing students for sustainability-driven practice requires integrating two fundamental dimensions: cultivating essential ESD competencies (Figure 1) such as systems thinking, critical thinking, and problem-solving skills that align with the Sustainable Development Goals (SDGs) [8,9,10,11,64,65], and strategically adopting digital tools to facilitate informed sustainable decisions.

Building Information Modeling (BIM) enables data-driven decision-making, performance analysis, and interdisciplinary collaboration, making it a priority for educational integration [12,13,14,15,16]. This integration ensures students develop the competencies and technological proficiency required for sustainable innovation. However, effectively integrating BIM into early design stages requires addressing the fundamental challenge of bridging BIM’s technical capabilities with the abstract, exploratory processes of conceptual design. The theoretical framework that follows examines the cognitive foundations of conceptual design, the role of diagrammatic reasoning in design exploration, and BIM’s current limitations and potential, establishing the conceptual basis for developing the D-BIM framework.

3.2. Conceptual Design in Architecture

Architectural design is a complex, creative, and open-ended process that is heavily reliant on tacit knowledge [17]. It exemplifies abductive reasoning, wherein designers generate specific instances from general concepts and available data [66]. Design knowledge often remains implicit, shaping a distinctive designer approach and decisions. Carrara et al. [67] categorize tacit design knowledge into three interrelated types:

• Descriptive knowledge: Defines the objects and concepts, detailing their functions, behaviors, and interactions. It describes what is being designed and how these elements interact.
• Normative knowledge: Outlines the goals, objectives, and constraints guiding the design process and underlying intentions.
• Operational knowledge: Provides methods for selecting design elements, assigning values, and establishing relationships to achieve specific objectives.

Architectural design progresses through interrelated phases. The American Institute of Architects (AIA) delineates three primary phases: schematic design, design development, and construction documentation [68]. Schematic design, also known as conceptual design, is the most challenging yet crucial phase.

Conceptual design establishes the foundation for design development by creating a framework that encompasses functional and programmatic requirements, formal and aesthetic responses, and structural and environmental considerations [28,69]. It relies on human creativity and cognitive activities such as abstraction, hypothesizing, analysis, and synthesis [18,19]. Horváth [18] defines it as the “systematized exploration and composition of applicable concepts” to determine optimal design alternatives that satisfy functional, economic, and technological requirements. Systems thinking aids this exploration by enabling designers to identify patterns, cause-and-effect relationships, and systemic interactions across formal, spatial, environmental, social, and programmatic dimensions [70,71].

Designers employ divergent and convergent thinking processes, positioning conceptual design as the intersection of creative ideation, problem-solving, and systems thinking [72]. This manifests as cycles of divergent techniques generating ideas, followed by convergent techniques that analyze and select concepts [73]. Kroll, Condoor, and Jansson [19] describe this as fluid movement between “configuration space” and “concept space,” involving abstraction (generalizing from specific configurations) and realization (translating abstract concepts into representations). Diagrams serve as critical mediators facilitating the transition from abstract thinking to tangible expressions [19].

3.3. Diagrammatic Reasoning

The term “diagram” originates from the Greek “dia-“ (across, out, or between two) and “gramma” (figure, mark, or line), reflecting its role in symbolizing ideas [25]. In architecture, diagrams are simplified graphical representations that convey essential spatial concepts [74] and serve as a pictorial language for knowledge production through abstract thinking [75].

Diagrams provide fundamental benefits in conceptual design. They encapsulate the logic of form [20] and mediate between abstract logical reasoning and physical architectural form [21]. They selectively highlight significant relationships while preserving necessary ambiguity for early design [76,77]. Their expressive capacity extends beyond formal logic to encompass spatial organization, contextual considerations, and performative constraints [75]. Additionally, diagrams facilitate collaborative problem-solving by externalizing intentions and enhancing communication in practice and education [77,78].

Diagrams can be classified by content and purpose. Alexander [79] introduced ‘constructive diagrams,’ synthesizing form diagrams (spatial and organizational aspects) and requirement diagrams (functional properties and constraints). This holistically represents form-function interaction in design. Diagrams also serve as generative (exploratory) and analytical (explanatory) tools. Analytical diagrams deconstruct existing works to uncover formal logic, while generative diagrams facilitate ideation through formal operations such as transformations, decomposition, and superimposition [21].

Despite their importance, diagrams are frequently obscured, implied, or disguised [21,25]. Complex translation from abstract concepts to physical form involves overlapping diagrammatic ideas that traditional analog media struggle to capture [25]. While digital media has enhanced the precision and efficiency of architectural design, its potential in the conceptual phase remains underexplored. This emphasizes the need to investigate how digital tools can better support and represent the complex, implicit design knowledge underpinning conceptualization [22,23,24,25].

3.4. Digital Tools and Conceptual Design

The evolution of digital design tools reveals progress and persistent challenges in supporting conceptual design. Early computer-aided design (CAD) systems were inadequate for conceptual exploration due to their emphasis on literal representation using geometrically specific data [80,81]. These systems failed to capture the progression from abstract, low-commitment diagrams to detailed, higher-commitment drawings, which are essential in early design [82]. Their deterministic approach prioritized automating drawing and documentation over supporting design exploration, lacking the ability to capture abstract thinking or handle complex geometries [23,83,84]. This relegated CAD to Computer-Aided Drafting rather than transformative design exploration [24,83,85,86].

Recognizing this limitation, Do and Gross [76] identified key features that computer programs should possess to support diagrammatic thinking effectively:

• Element and Relationship Definition: Enabling designers to define elements and establish relationships as constraints, creating flexible yet structured design frameworks.
• Diagram Transformation: Facilitating conversion between diagrams, supporting the iterative design process through multiple design variations.
• Multi-level Abstraction: Allowing representation at various abstraction levels, bridging conceptual ideas and concrete designs.

Recent advancements in digital design tools, such as Building Information Modeling (BIM), simulation, and parametric modeling, have enhanced architectural design support [22]. These tools transition from fragmented representations to intelligent building models that embed design information [22,23,24]. Early-stage integration of performance simulation and environmental analysis tools has demonstrated particular efficacy in supporting conceptual processes. These facilitate rapid, iterative feedback on critical parameters, such as massing, orientation, and geometry, that heavily influence building performance [87,88]. Research demonstrates that early-stage simulation integration improves energy efficiency and cost-effectiveness, while reducing environmental impact, whereas delayed analysis often results in suboptimal outcomes [88,89]. Nevertheless, further research is needed to investigate how these tools can enhance conceptual diagramming processes, which are essential for developing and communicating design knowledge in architecture [32,81].

3.5. Building Information Modeling (BIM)

Building Information Modeling (BIM) has transformed the architecture, engineering, and construction (AEC) industry [90]. The US National BIM Standard (2007) [91] defines BIM as “a digital representation of physical and functional characteristics of a facility and a shared knowledge resource for information about a facility forming a reliable basis for decisions during its life cycle” (p. 149). BIM operates as an object-based parametric modeling system where building elements are information-rich objects defined by rules and parameters governing geometric and non-geometric properties [92]. This creates dynamic models that streamline design by automatically propagating changes across interconnected elements while preserving design intent throughout the project lifecycle [28].

BIM offers significant benefits across all phases of design. In pre-construction, BIM facilitates design development, feasibility studies, performance analysis, and construction simulation. It enables early multi-disciplinary collaboration and reduces errors in construction drawings [93]. During construction, BIM synchronizes processes, reveals problems, detects clashes, and aids component manufacture [90]. In post-construction, it supports facility management through efficient information management and exchange [93,94].

Despite these advantages, BIM applications predominantly focus on technical aspects in both practice and education. Boeykens [29] asserts that the full advantages of BIM can only be realized when utilized throughout every phase of a building’s life cycle, starting from the early design stages. Early BIM adoption can enable the analysis of critical initial decisions, including building morphology, massing, orientation, and window-to-wall ratios, which significantly impact energy performance [95,96,97,98,99]. However, this potential remains largely untapped. Several scholars advocate for exploring the conceptual design potential of BIM [28,30,32,100], although this remains a challenge for researchers, educators, and practitioners.

In architectural design education, BIM application has typically been limited to construction techniques, material properties, and cost data [30,101]. This narrow focus overlooks BIM’s conceptual design potential, underscoring the need for research that addresses the relationships between BIM and conceptual design within studios [30,102,103].

However, integrating BIM into conceptual design faces significant challenges:

• Techno-centric Perception: BIM is viewed as a technical tool rather than a design medium [104], perceived as too sophisticated for early design stages [26].
• Constraints on Creativity: BIM may hinder creativity by imposing predefined architectural ontologies based on component libraries, leading to designs that become mere assemblages of software-defined elements rather than creative explorations [26,105,106].
• Limited Architectural Language: While BIM exemplifies construction language, it lacks architectural language, including forms, concepts, relationships, and aesthetics [27].
• Cognitive Disconnect: Akin [31] argues that designers cannot rely on their intuitive cognitive skills when using BIM because these tools lack intuitive interfaces that connect designers’ mental models to the software’s internal functions. This disconnect creates barriers during conceptual design, which relies heavily on tacit thinking processes.

To address these challenges, researchers have proposed several strategies:

• Conceptualize BIM as a knowledge system that aligns with architectural theories [27,32,107]. The formal language theory is particularly relevant, as it reflects BIM’s object-oriented data structures [108,109]. Building on Barthes’s [110] linguistic framework, BIM can be structured through: the “dissection” of architectural form into vocabularies and their “articulation” into a formal system through association rules and constraints.
• Employ a divide-and-conquer strategy to simplify the complexity of BIM by breaking down solutions into partial solutions that can be reassembled [31].
• Utilize abstract massing tools for rapid form exploration through low-fidelity models [33]. These models facilitate performative design and simulation [111], proactively identify design problems, and foster interdisciplinary collaboration [112].
• Integrate diagrammatic reasoning and parametric modeling to support systems thinking and relation-oriented design thinking. This approach provides broader abstractions that support the creative exploration of design relationships, while enabling dynamic and adaptable models through defined rules and constraints [27,32,33,113,114].
• Encode design semantics to capture and express intangible, abstract, and theoretical design information [32,107,115].

Despite these theoretical strategies, current parametric and diagrammatic workflows in digital tools, particularly BIM, remain fragmented. They address either detailed design phases or remain limited to isolated computational exercises focusing on specific design aspects—such as structural systems, spatial planning, or performance metrics—rather than the integrated design process where all considerations must be synthesized [30,31,116,117]. Recognizing this limitation, this research develops a diagrammatic BIM framework that enables systematic progression from abstract concepts to comprehensive architectural solutions, bridging BIM’s technical capabilities with conceptual exploration.

4. Diagrammatic BIM Framework (D-BIM)

The D-BIM framework utilizes Autodesk Revit as its primary tool, establishing a system aligned with formal language theory. It adopts a “divide and conquer” strategy to hierarchically decompose formal architectural systems into manageable subsystems (Figure 2). D-BIM establishes three critical relationships within Revit’s modeling environments: dissection, articulation, and actualization (Figure 3).

Dissection takes place in the Family Editor (FE), where parametric vocabulary libraries are created. This vocabulary encompasses construction components (e.g., roofs, walls) and abstract design elements (e.g., planes, volumes). For Meier’s architectural language, two types of conceptual families were developed: primitive vocabulary and composite vocabulary (combinations of primitive vocabularies). Relationships and dependencies are defined through parameters (e.g., dimension parameters, formulas) and constraints (e.g., alignment, equality). The construction method of each vocabulary element determines its transformational behavior when parameters are modified. These vocabularies are developed as semantically rich parametric families that incorporate text and material parameters.

Articulation occurs within the Conceptual Design Environment (CDE), serving two primary functions: generating subformal systems as primary syntactic units and integrating these units with vocabulary (developed in FE) to construct principal diagrams. A syntactic unit comprises multiple elements governed by relational rules. Configuration relies on establishing relationships between syntactic units and conceptual vocabulary through reference lines, planes, parameters, and constraints. The workflow begins with creating a syntactic unit that defines the main rectangular mass, along with its parametric spatial layering system. This mass is refined through additions and subtractions using the conceptual vocabulary, with type parameters distinguishing between solid and void elements. The resulting parametric diagram explicitly defines formal relationships such as proportions, modularity, and axiality. Visual legibility is enhanced through color-coded regulating lines, planes, text, and material parameters.

Actualization transforms diagrams into built forms in the Project Environment (PE) by applying construction vocabulary. This integration of abstract representations with physical manifestations enables dynamic updates across both abstract and physical elements. The framework incorporates dual evaluation components. Quantitative analysis utilizes performance analysis tools to measure factors such as energy performance. Qualitative analysis employs real-time rendering tools (RTRs) to visualize and assess spatial and aesthetic qualities.

The integrated D-BIM approach enables systematic progression from abstract concepts to detailed architectural solutions while maintaining design integrity. It propagates changes across all environments while generating multiple design alternatives from a single parametric model. The framework supports both technical and aesthetic decision-making during conceptual design by integrating low-fidelity models for performance analysis with high-fidelity models for spatial evaluation. This framework was validated through an empirical study that tested its effectiveness in supporting early design processes within the context of Richard Meier’s architectural language.

5. D-BIM Validation

5.1. The Empirical Study

A 14-week empirical study was conducted with third-year architecture students designing a 3000 m² public library to validate the D-BIM framework. This study tested the framework’s capacity to support conceptual design through diagrammatic reasoning and parametric modeling, encompassing phases of knowledge building (weeks 1–4), design development (weeks 5–10), and project refinement (weeks 11–14) (Figure 4).

The knowledge-building phase established foundations for the design process. Over four weeks, students engaged in precedent studies, site analysis, program development, and BIM workshops. They familiarized themselves with the D-BIM framework and linkages between Revit’s three environments through hands-on tutorials. Students analyzed Richard Meier’s architectural language through morphological studies, group discussions, and instructor-led lectures, followed by analytical exercises. This analysis established the foundations that guided students in translating concepts into tangible forms, while considering functional, formal, and contextual dimensions.

The development phase employed the D-BIM framework for design exploration. In the CDE, students created a primary syntactic mass with a parametric spatial layering system to establish an initial spatial organization. Simultaneously, they developed stacked parametric program diagrams using color-coded and labeled families created in the FE. These diagrams defined public and private spaces along two key perpendicular axes: a transverse axis that creates a passage through the form and a longitudinal axis that defines horizontal circulation (Figure 5).

Students further articulated the primary syntactic mass through addition and subtraction operations. At the axial intersection, they established a syntactic center—a multi-volumetric space featuring floated platforms and freestanding circular columns to house primary functions. Guided by their library concepts and contextual responses, students employed constraints, regulating lines, and parameter associations to define the orientation and location of significant components and axes. Program diagrams were iteratively updated to ensure form–function cohesion as designs evolved (Figure 5).

The refinement phase focused on actualization, evaluation, and iterative design. In PE, students actualized diagrams by attaching construction elements. This phase enabled iterative workflows where students modified diagrams by adjusting parameters directly in the PE or updating the CDE and reloading the changes. This bi-directional workflow supported the exploration of options while maintaining conceptual consistency. Students utilized predefined templates with specific views to facilitate reflection and self-evaluation. These views employed visibility settings to highlight key design aspects across various projections. Section views diagrammed public/private patterns, axiality, and syntactical centrality (Figure 6). Plan views illustrated functional patterns, circulation, proportional systems, and part–whole relationships. Elevation views demonstrated geometric relationships and modularity (Figure 7). Isometric views created constructive morphological diagrams, highlighting the chronology of form development (Figure 8).

During the development and actualization phases, Enscape was integrated for qualitative design evaluation (Figure 8). Students used Enscape to generate real-time renderings and walkthrough visualizations, enabling them to experience their designs from a user perspective. This immersive evaluation revealed critical design qualities, including scale appropriateness, visual connections, and lighting distribution through experiential navigation. Students could virtually test design decisions, identify issues, and then return to parametric models for targeted adjustments. This real-time feedback loop created an evaluation methodology where experiential qualities informed technical parameters.

Environmental analysis served as an educational introduction to performance-informed design thinking, rather than as a comprehensive decision-driving analysis (Figure 8). Wind analysis informed basic site planning decisions by examining the relationships between wind patterns and activities such as sitting, standing, or walking based on building morphology. This introduced students to how architectural form creates microclimatic conditions, demonstrating how building heights, orientations, and geometric configurations generate wind acceleration zones, downdrafts, and sheltered areas. Solar analysis introduced correlations between solar radiation, interior programming, and window-to-wall ratios. Students explored relationships between solar exposure data and spatial functions, considering daylight-dependent space positioning. Students began to examine how formal decisions might impact the potential for renewable energy generation, establishing connections between architectural form and building performance.

The empirical study explored the potential of the D-BIM framework in integrating diagrammatic reasoning, parametric modeling, and introductory performance analysis. The framework facilitated transitions between abstract thinking and formal expressions, enabling students to understand formal system construction as a network of subsystems that respond to programmatic, spatial, experiential, and environmental factors.

5.2. Survey

5.2.1. Survey Reliability

The 13-item survey demonstrated good internal consistency at pre-test (Cronbach’s α = 0.83) and acceptable reliability at post-test (α = 0.72), both exceeding the conventional threshold of 0.70. The moderate decrease in reliability reflects increased response variability following the intervention. The maintained acceptable reliability validates pre-post comparisons and supports the subsequent statistical analysis. Analysis of total improvement scores revealed heterogeneous response patterns, with 5 participants (26%) showing limited improvement (mean = 16.2 points) while 14 participants (74%) demonstrated substantial improvement (mean = 27.6 points), indicating differential framework effectiveness across participants.

5.2.2. Statistical Analysis and Results

Based on the validated measurement instrument, pre-post survey analysis revealed statistically significant positive changes across all measured domains. The 13-item survey was organized into four thematic clusters: students’ perception of the design process (Q1–3), the role of diagrams (Q4–5), Revit utilization (Q6–9), and self-efficacy (Q10–13) (

Table 1. Pre–post test survey questions.

Theme	Number	Question
Students’ Perception of The Design Process	Q1	To what extent do you agree that architectural design follows a systematic, logical process of conceptualization and development?
	Q2	I can describe the primary conceptual elements and vocabulary of my architectural design project.
	Q3	I can effectively describe and explain the fundamental rules and syntactical principles guiding my design approach.
The Role of Diagrams	Q4	Diagrams provided critical analytical insights into my design conceptualization.
	Q5	Diagrams played a significant generative role in developing my form and organizing design elements according to predefined rules.
Revit Utilization	Q6	Rate how Revit contributed to creating architectural forms in your design.
	Q7	Rate how Revit aided in selecting and defining design elements/vocabulary
	Q8	Rate how Revit helped in establishing and defining design rules.
	Q9	Rate how Revit supported the development and elaboration of your formal ideas
Self-Efficacy	Q10	How certain you are that you can use digital media to communicate your design ideas
	Q11	How confident you are in using digital media to design
	Q12	How would you rate your overall architectural design skills
	Q13	How confident you are that you can make design decisions, solve problems, and accomplish your goals

). Descriptive and inferential statistical analyses demonstrated the framework’s impact across these domains (

Table 2. Descriptive statistics for pre–post survey responses.

	Central Tendency Measures				Variability Measures				Skewness Measures
	Mean (M)		Median (Mdn)		Standard Deviation (SD)		Range		Skewness (SE)
	Pre-Test	Post-Test	Pre-Test	Post-Test	Pre-Test	Post-Test	Pre-Test	Post-Test	Pre-Test	Post-Test
Q1	2.78	4.63	3	5	0.91	0.49	3	1	−0.50	−0.59
Q2	3.21	4.47	3	4	0.91	0.51	3	1	−0.94	0.11
Q3	2.78	4.79	3	5	1.23	0.41	4	1	−0.15	−1.54
Q4	3.00	4.42	3	4	0.94	0.83	4	3	−4.13	−1.62
Q5	3.15	4.11	3	4	1.01	0.81	4	3	−0.70	−0.91
Q6	1.26	4.63	1	5	0.93	0.49	4	1	3.99	−0.60
Q7	1.26	4.37	1	4	0.73	0.68	3	2	3.31	−0.63
Q8	1.21	4.52	1	5	0.71	0.51	3	1	3.77	−0.11
Q9	1.11	4.36	1	4	0.46	0.68	2	2	4.35	−0.63
Q10	2.95	4.37	3	4	0.97	0.68	4	2	0.11	−0.63
Q11	2.95	4.21	3	4	0.97	0.78	4	2	−0.29	−0.41
Q12	3.21	4.00	3	4	0.71	0.57	2	2	−0.34	0.00
Q13	3.78	4.36	4	4	0.78	0.68	2	2	0.41	−0.63

and

Table 3. Inferential statistics and effect size estimates.

	Wilcoxon Signed-Rank Test Two-Tail, p-Value (p < 0.05)			Hodges–Lehmann Median Difference
	P	Z	Effect Size *	Median Difference	95% CI
Q1	0.00030	−3.620	−0.587	2.0000	[1.5000, 2.5000]
Q2	0.00030	−3.621	−0.587	1.0000	[1.0000, 1.5000]
Q3	0.00020	−3.724	−0.604	2.0000	[1.5000, 2.5000]
Q4	0.0006	−3.432	−0.556	1.5000	[1.0000, 2.0000]
Q5	0.0083	−2.636	−0.427	1.0000	[0.5000, 1.5000]
Q6	0.00020	−3.723	−0.604	3.5000	[3.0000, 4.0000]
Q7	0.0001	−3.879	−0.629	3.0000	[2.5000, 3.5000]
Q8	0.0001	−3.904	−0.633	3.5000	[3.0000, 3.5000]
Q9	0.0001	−3.893	−0.632	3.5000	[3.0000, 3.5000]
Q10	0.00064	−3.408	−0.553	1.5000	[1.0000, 2.0000]
Q11	0.00194	−3.102	−0.503	1.5000	[0.5000, 2.0000]
Q12	0.00512	−2.803	−0.455	0.5000	[0.5000, 1.0000]
Q13	0.01278	−2.653	−0.404	0.5000	[0.0000, 1.0000]

* Effect size (r) = Z/√N: (N = the total number of observations).

).

Mean ratings increased by 0.6 to 3.37 points across domains (Figure 9), with median shifts from 1–3 to 4–5, indicating convergence towards higher competency perceptions. Wilcoxon signed-rank analysis revealed statistically significant positive shifts from pre-test to post-test across all survey measures (p < 0.05) with moderate (|r| >= 0.3) to large effect sizes (|r| >= 0.5) (Table 3).

• Design Process Understanding: Improvements were observed in students’ perception of design as a systematic process (Q1, r = −0.587, p = 0.00030), ability to describe design elements (Q2, r = −0.587, p = 0.00030), and rules (Q3, r = −0.604, p = 0.00020). Students demonstrated enhanced recognition of diagrams’ analytical role in communicating ideas and explaining form development (Q4, r = −0.556, p = 0.0006) and their generative role in developing architectural forms (Q5, r = −0.427, p = 0.0083). Hodges–Lehmann median differences indicate typical improvements of 1.0–2.0 points on the 5-point scale (Table 3).
• BIM Tool Integration: The most substantial changes were evident in Revit’s perceived contribution to various aspects (Q6–9). Revit’s role in creating architectural forms (Q6) showed improvement (r = −0.604, p = 0.00020). Similar patterns were noted in Revit’s perceived aid in selecting elements (Q7, r = −0.629, p = 0.0001), determining rules (Q8, r = −0.633, p = 0.0001), and supporting formal idea development (Q9, r = −0.632, p = 0.0001). These items showed the largest practical improvements, with Hodges–Lehmann estimates of 3.0–3.5 points (Table 3).
• Academic Self-Efficacy: Participants reported increased self-efficacy across multiple domains. Confidence in using digital media for communication (Q10, r = −0.553, p = 0.00064) and practice (Q11, r = −0.503, p = 0.00194) increased. Self-reported skills (Q12, r = −0.455, p = 0.00512) also showed positive change. Notably, participant self-efficacy in making decisions, solving problems, and accomplishing goals (Q13, r = −0.404, p = 0.01278) demonstrated a positive shift. Self-efficacy improvements were more modest, with median differences of 0.5–1.5 points (Table 3).

5.3. Multi-Source Observations and Constraints

The empirical study’s observations revealed several aspects that provide preliminary insights into the D-BIM framework’s potential in supporting early-stage architectural design:

• Enhanced Design Iteration and Exploration: Participants demonstrated improved design articulation and exploration, reporting increased ability to generate and evaluate multiple iterations compared to their experiences in earlier studios. The framework’s parametric nature enabled rapid design modifications while maintaining design logic.
• Improved Abstraction-to-Realization Transition: The D-BIM framework appeared to facilitate smoother transitions between abstraction levels, potentially addressing the challenge of preserving design intent from diagrams to detailed models. Participants noted enhanced understanding of how abstract formal concepts translate into tangible architectural forms. This contrasted with students’ reported previous experiences in earlier studios, where they struggled to move from unscaled conceptual sketches to scaled architectural models. The D-BIM framework creates scaled parametric diagrams linked to architectural expression, enabling simultaneous development of both abstract thinking and concrete realization.
• Enhanced Design Communication: The framework’s parametric diagrams made design logic explicit and discussable, enabling both productive peer learning through cross-project comparison and individual self-evaluation through analytical templates. Diagrammatic reasoning facilitated critique sessions that emphasized design as a structured process, contrasting with participants’ prior studios, where limited shared diagrammatic frameworks hindered collaborative learning.
• Progressive Skill Development: Despite an initially steep learning curve, participants gradually became more adept with the D-BIM framework. Over the study period, students’ work appeared to show increased sophistication in parametric relationships and design articulation.

An independent peer debriefer participated in critique sessions at weeks 6, 9, and 14. The debriefer noted that participants initially struggled to articulate design intentions but showed improvement in expressing design rationale by week 9, which appeared to be associated with increased facility with parametric diagrams and iterative modeling processes. The final week 14 critique suggested apparent advancements in design complexity, with 14 of 19 participants meeting the framework criteria.

The empirical study also revealed several implementation constraints:

• Instructional Challenges: The high instructor-to-student ratio (1:19) hindered individualized support, potentially impacting the framework’s effectiveness across diverse student needs.
• Conceptual Translation Difficulties: Some students struggled to translate abstract concepts into parametric relationships, often limiting their design exploration. Specifically, they had difficulty understanding how an element could be simultaneously represented as both abstract and concrete components. These students required focused support to develop this dual-level thinking.
• Time Constraints: The intensive studio schedule and the substantial amount of new content limited students’ capacity to master all the components of the framework, which was evident in the inconsistent application of Forma. This suggests extending the D-BIM framework beyond one semester to allow gradual integration of all the framework components.
• Workflow Limitations: Integrating environmental analysis tools exposed interoperability issues between Revit and Autodesk Forma. The absence of direct parametric data transfer required manual re-exports after every change, disrupting the iterative design process. In contrast, Enscape’s seamless integration with live parametric links enabled continuous environmental analysis. As a result, students preferred and used Enscape more consistently for design evaluation, while Forma use was infrequent due to inefficient workflow.

6. Discussion and Conclusions

6.1. Research Problem and Framework Response

This research addressed the limited capacity of Building Information Modeling (BIM) to support conceptual design processes that rely on abstract thinking and diagrammatic reasoning. BIM demonstrates potential for sustainable design through data-driven decision-making; however, its application during early design stages—where critical sustainability decisions are made—remains constrained by perceived complexity and inadequate support for exploratory design processes.

This study investigated whether BIM tools can support conceptual design by embedding diagrammatic reasoning, testing two hypotheses: that students using the D-BIM framework would demonstrate an ability to use BIM for conceptual design (H1) and exhibit enhanced diagrammatic reasoning and systematic design thinking abilities (H2). The diagrammatic BIM (D-BIM) framework addressed these limitations through three integrated relationships within Revit, dissection, articulation, and actualization, enabling systematic progression from abstract concepts to detailed architectural forms.

6.2. Key Findings

The empirical investigation provided evidence supporting both research hypotheses across three interconnected domains. Survey data indicated significant improvements in BIM tool utilization measures, with participants reporting increased confidence in Revit’s ability to create architectural forms, select design elements, and define design rules. These quantitative findings were supported by observational evidence showing that most students successfully employed the D-BIM framework for iterative design development.

The framework’s effectiveness is centered on parametric diagrams that served dual pedagogical functions. Survey results suggested improved recognition of diagrams’ analytical and generative roles, while observational data revealed that parametric diagrams facilitated both productive peer learning and individual self-evaluation. Accordingly, design critique sessions were transformed from product-focused evaluations to process-oriented discussions.

Evidence from multiple sources suggested enhanced systematic design thinking capabilities. Students demonstrated improved abstraction-to-realization transitions and appeared to successfully navigate the conceptual challenge of translating abstract ideas into parametric representations. However, differential effectiveness emerged as a critical finding: while 74% of participants demonstrated substantial improvement, 26% showed limited progress. The survey reliability decreased from α = 0.83 to α = 0.72, although it remained acceptable, reflecting increased response heterogeneity. This pattern suggests that the framework facilitated distinct learning trajectories, which may depend on students’ capacity for dual-level thinking that simultaneously operates at both abstract and concrete levels.

6.3. Study Contributions

This research addresses barriers in BIM education, including perceived complexity and constraints on creativity imposed by predetermined component libraries. Drawing on Carrara et al.’s [67] framework of design knowledge, the D-BIM approach illustrates how BIM can systematically capture and integrate descriptive knowledge, normative knowledge, and operational knowledge while incorporating Alexander’s [79] concept of ‘constructive diagrams’ to enable both analytical deconstruction and generative exploration.

The D-BIM approach fulfills Do and Gross’s [76] criteria for digital diagrammatic thinking through: element and relationship definition via parametric families; diagram transformation through iterative modeling; and multi-level abstraction via a three-environment workflow (dissection, articulation, and actualization). As a result, the framework facilitates enhanced design communication, iterative design processes, and seamless abstraction-to-realization transitions through parametric continuity.

Beyond technical contributions, the framework demonstrates alignment with Education for sustainable development competencies (

Table 4. Integration of UNESCO ESD competencies within the D-BIM framework implementation.

ESD Competency	D-BIM Achievement	Level of Integration *
Systems Thinking	Students developed an understanding of formal system construction as parametric networks within the architectural domain.	Advanced
Anticipatory Thinking	Exploratory environmental analysis through Autodesk Forma, considering solar exposure, wind patterns, and basic building performance implications.	Basic
Critical Thinking	Analytical exercises with architectural precedents and design alternative evaluation through explicit parametric logic and iterative assessment.	Intermediate
Strategic	Structured academic project methodology with systematic tool integration.	Moderate
Collaboration	Enhanced design communication and peer learning through shared diagrammatic frameworks.	Advanced
Integrated Problem-Solving	Multi-faceted design challenges integrating technical and creative considerations. Limited engagement with broader sustainability problems	Intermediate
Self-awareness	Metacognitive awareness of design thinking progression and skill development. Limited reflection beyond academic learning context.	Basic
Normative	Values-based decision-making through parametric constraints balancing formal, functional, and basic environmental criteria within design framework.	Intermediate

* Integration Levels: Basic: Introductory exposure or limited application within study context; Intermediate: Competency development within study scope with clear evidence but contextual limitations; Advanced: Strong competency development with substantial evidence and broader applicability; Expert: Comprehensive mastery with independent application across contexts (not achieved in this study).

), suggesting pathways for competency-based digital pedagogy in architecture. The framework showed substantial development in systems thinking and collaboration competencies, though comprehensive ESD implementation remains limited by the study’s scope and single architectural language focus.

6.4. Study Limitations

Several constraints limit the generalizability of findings. The single-group pre-post design from a single institution (n = 19) and the lack of a control group limit definitive causal attribution and restrict external validity. The study’s scope was constrained to Richard Meier’s Western modernist architectural language within third-year studio work, necessitating evaluation across diverse design approaches to establish broader validity.

Framework transferability appears dependent on architectural language systematization. The D-BIM approach may be adaptable to design systems that utilize defined vocabularies and rule-based relationships—such as approaches that employ geometric primitives with clear proportional and modular rules. However, any extension would require empirical validation in each new context. Vernacular and culturally specific designs may need modified workflows to address less systematic formal relationships. Organic design languages that emphasize continuous deformation would likely demand fundamentally different parametric approaches.

Additional study limitations include the limited integration of environmental analysis, with structural, material, and lifecycle factors not addressed in this study. Implementation constraints included time limitations, workflow challenges between software platforms, and difficulties some students experienced with dual-level thinking requirements.

6.5. Implications and Future Research

Future validation requires controlled experimental designs across multiple institutions and diverse architectural languages to establish broader applicability. Longitudinal studies would assess skill retention and professional practice transfer, while comprehensive sustainability integration research should address structural, material, and lifecycle considerations alongside improved software integration and pedagogical strategies that cater to diverse learning capabilities.

The framework suggests practical applications across educational and professional contexts. For educators, structured tutorials and peer mentoring systems can support students with varying abilities in abstract reasoning. Curriculum development should implement a progressive D-BIM complexity, beginning with formal system construction and then advancing to practical building considerations in upper-level studios. This scaffolded approach enables systematic skill development while maintaining pedagogical coherence across the curriculum.

In professional practice, the framework’s externalization of design logic through parametric diagrams may improve interdisciplinary communication and streamline transitions from conceptual abstraction to detailed specification. The approach may also be applicable to other engineering disciplines that require early-stage diagrammatic reasoning. Software developers should prioritize seamless parametric data transfer between modeling and analysis platforms to eliminate current workflow disruptions.

6.6. Conclusions

This research provides preliminary evidence that Building Information Modeling can support conceptual design when integrated with diagrammatic reasoning and parametric modeling. The D-BIM framework enabled students to develop systematic design thinking while maintaining creative exploration, thereby challenging perceptions of BIM as a purely technical tool.

The study demonstrates that diagrammatic reasoning bridges abstract conceptual thinking with BIM’s computational capabilities. Parametric diagrams served dual pedagogical functions, supporting both peer learning and individual self-evaluation while transforming design critiques from product-focused to process-oriented discussions.

The research contributes preliminary evidence that digital tools can enhance rather than constrain conceptual thinking when supported by appropriate pedagogical frameworks. However, broader validation across diverse architectural languages and controlled experimental designs remains necessary to establish transferable principles for architectural design education.