Material thinking and digital craft: Knitting techniques as a design pedagogy in architectural education

Evolution of knitting techniques in architecture and design

While knitting techniques have long been associated with the textile industry, recent developments have led to their exploration within architectural design as a means of generating flexible spatial solutions. The incorporation of knitting into architectural practice contributes significantly to both the aesthetic and functional dimensions of design. Wintermantel et al. ⁴ investigated the application of knitted materials in the field of biotechnology, emphasizing their potential to enhance architectural performance through optimized properties such as tensile strength and flexibility. Zheng et al. ⁵ explored how minimal surface geometries can improve the mechanical performance of knitted structures, while Knittel et al. ¹ and Oghazian et al. ³ examined the integration of knitting techniques into digital modeling and fabrication workflows. Collectively, these studies demonstrate that knitted systems offer unique advantages in architectural applications, including lightweight construction, adaptability, and resilience, positioning them as valuable tools for innovation in contemporary design.

The role of digital design and fabrication technologies in advancing knitting techniques

The advent of computer-aided design (CAD) and computer-aided manufacturing (CAM) technologies has enabled the transformation of knitting techniques from conceptual digital models into precise physical prototypes. This digital-to-physical workflow enhances the fidelity and flexibility of knitted architectural systems. Baranovskaya and Prado, for instance, investigated the integration of knitted textiles with pneumatic systems to explore the potential for lightweight, deployable structures. ⁶ Similarly, Ahlquist et al. analyzed the behavioral interaction between pneumatic mechanisms and knitted assemblies to understand their spatial dynamics. ⁷

Novak (2020) and Tamke et al. (2021) further advanced this discourse by illustrating how parametric modeling and CNC-based fabrication, including 3D printing, can be utilized to produce customizable knitted forms.^2,8 These digital tools not only support the production of complex geometries with high precision but also expand the material and structural capabilities of knitted systems in architectural contexts. In the present study, CAD/CAM processes were also used to test fabrication parameters—such as filament type, infill density, and layer height—in relation to the physical performance of knitted-inspired geometries.

Integration of knitting techniques and digital tools in architectural education

Incorporating knitting techniques into architectural education provides students with an opportunity to engage in creative, material-driven design processes enhanced by digital technologies. Chaturvedi et al. (2011) and Lindsay (2015) evaluated the aesthetic and functional capacities of knitted systems and addressed their pedagogical adaptation for architectural learning environments.^9,10 Ahlquist et al. demonstrated how digital simulations and knitted forms could be integrated into architectural projects to encourage spatial experimentation. ⁷ Scott et al. emphasized that the fusion of digital tools with textile-based techniques fosters students’ creative problem-solving skills and supports the development of design thinking. ¹¹

Introduction of knitting techniques and artistic applications

The design process was initiated with an emphasis on knitting as both an artistic and architectural expression, aiming to merge aesthetic intent with spatial functionality. At this stage, students drew inspiration from Lindsay’s exploration of artistic knitting techniques, which emphasize textile-based aesthetics, and from Glazzard’s experimental formal studies that demonstrated the expressive potential of knitted geometries.^10,13 In addition, Underwood’s (2009) work on 3D knitted shaping methods was introduced, illustrating the transformative capacity of knitting in design. ¹⁴ His research focused on the architectural potential of industrial flatbed knitting machines, such as those developed by Shima Seiki, highlighting their ability to produce complex three-dimensional forms with high efficiency and structural flexibility.

Visual references of prominent knitting and 3D knitting examples from the literature were presented during studio sessions to guide students in their design explorations and material choices (Figure 2).OPEN IN VIEWER

Material exploration: Creative form-finding and textural research

In the early phases of the studio, students conducted exploratory studies to investigate the surface potential of knitted structures. Drawing inspiration from natural textures, they developed multi-layered surfaces, transitional patterns, and complex textural compositions. These studies enabled a deeper understanding of how knitted materials could evoke spatial and aesthetic complexity. Representative samples of these textural explorations are illustrated in Figure 3.OPEN IN VIEWER

Experimentation with material types: Understanding material behavior

In this study, a range of yarn types with varying fiber compositions and thicknesses was employed to explore the integration of knitting techniques into architectural design education. The material selection prioritized diversity in fiber content and yarn density to facilitate different tactile and visual outcomes. The specifications of the yarns used are as follows:

(1) Content: 100% Cotton - Density: 500.0 tex/4500.0 denier - Thickness: 2.0 Nm

Beyond their compositional and dimensional differences, these yarn types exhibited distinct tendencies to assume specific knitted forms, indicating that fiber structure and density influenced not only tactile qualities but also the morphological behavior of the resulting geometries.

To evaluate the properties of different yarn types, students performed knitting experiments using a Sentro 48-Needle Smart Circular Knitting Machine, a device commonly used in textile prototyping. Yarns with varying levels of flexibility, durability, and weight were tested. Through hands-on experimentation, the students gained insight into the limitations and potential of different yarn materials. Detailed outcomes of these trials are presented in Figure 4.OPEN IN VIEWER

Subsequently, students were encouraged to expand their material repertoire by identifying and testing alternative, non-traditional materials that could be knitted. These included unconventional substances capable of deformation and interlocking, offering new design possibilities. Examples of these material-based explorations are shown in Figure 5.OPEN IN VIEWER

Comparative analysis indicated that yarns with higher cotton content exhibited greater dimensional stability under tension, producing knitted geometries with reduced deformation. Conversely, acrylic-dominant blends provided higher elasticity, which facilitated more complex curvature but required additional structural support in physical prototypes. These material–structural correlations informed subsequent parametric modeling decisions, such as adjusting aperture ratios to match the tensile performance of each material.

Physical prototyping: From 2D surfaces to 3D forms

Beyond digital simulations, students produced small-scale physical models to gain a deeper understanding of the mechanical and spatial behavior of knitted materials. These prototypes were used to investigate, surface geometries, and dynamic deformations. Figure 6 presents selected examples of these exploratory knitting models developed during the course.OPEN IN VIEWER

To extend this work into volumetric spatial inquiry, students tested how two-dimensional knitted surfaces could be transformed into three-dimensional architectural forms. By incorporating tension-based systems and supportive structural frameworks, they examined the potential of knitted surfaces to assume self-supporting and complex geometries. These applications formed a critical part of the learning process, as they enhanced students’ comprehension of the material–space relationship. Figure 7 illustrates examples of the initial three-dimensional physical transformations generated by students.OPEN IN VIEWER

Artificial intelligence–assisted form-finding

Building upon their physical prototypes, students were asked to transfer their initial knitted models into digital environments and generate alternative design iterations using artificial intelligence–based platforms. Tools such as Midjourney, DALL·E, RunwayML, and Artbreeder were utilized to explore visual and morphological possibilities beyond conventional form-generation techniques.

These AI tools enabled students to broaden their visual language, reinforce intuitive decision-making, and transcend standard architectural typologies. By leveraging algorithmic variation and image-based transformation capabilities, students envisioned spatial manifestations of knitted logic and expanded the scope of speculative architectural design. Selected outputs from these AI-driven studies are presented in Figure 8, showcasing a range of architectural forms that reinterpret knitting as a spatial, generative strategy.OPEN IN VIEWER

Digital simulation and parametric design applications

Computer-aided manufacturing (3D printing)

Final design outcomes

The diverse knitting techniques explored throughout the studio were synthesized into original spatial systems, with each student constructing a surface-based design that functioned as a structural shell. These final outputs reflected cumulative learning outcomes from earlier phases, including material exploration, digital modeling, and fabrication.

Students critically reflected on their process-based knowledge and produced three-dimensional architectural designs that were original, functional, feasible, resilient, and aesthetically sophisticated. These results demonstrate how knitting, when coupled with digital technologies, can serve as a pedagogical framework that fosters material-based exploration and iterative learning in architectural education (Figure 11).OPEN IN VIEWER

Methods and models for evaluating pedagogical impact during the learning process

Learning styles are commonly defined as relatively stable cognitive, affective, and physiological traits that indicate how individuals perceive, interact with, and respond to learning environments.^15,16 Within this broad construct, Gregorc conceptualized learning styles along a continuum from “concrete–sequential” to “abstract–random”. ¹⁷ James and Galbraith proposed a sensory-modality-based classification distinguishing visual, auditory, tactile, and kinesthetic preferences, ¹⁸ while Barsch articulated a closely related set of perceptual dimensions. ¹⁹ Building on Jung’s theory of psychological types, Myers and Myers formulated a typology comprising eight characteristic learning styles. ²⁰ In parallel, Ehrman and Oxford developed a model grounded in cognitive functions to account for individual differences in learning behavior. ²¹

Among these, Kolb’s Experiential Learning Theory (1984) has become one of the most widely utilized frameworks, defining learning as a four-stage cyclical process: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE). Based on this cycle, Kolb proposed four dominant learning styles—accommodating, converging, assimilating, and diverging—that reflect individuals’ preferences for acquiring and processing knowledge. In practice-based disciplines such as architecture, this model is particularly effective, as it aligns well with pedagogical activities such as model-making and digital prototyping (CE), critical reflection (RO), conceptual development (AC), and fabrication or design testing (AE). As such, Kolb’s model provides a robust pedagogical foundation that supports both the creative and analytical dimensions of architectural education. We focus on Kolb’s Experiential Learning Theory, as it maps directly onto our studio sequence—knitted material trials (CE), critique-led reflection (RO), parametric reasoning (AC), and fabrication tests (AE). ¹² (Figure 12).OPEN IN VIEWER

Kolb’s learning cycle served as a core pedagogical framework in this study, enabling students to experience knitting techniques through hands-on engagement and to reinforce those experiences via digital design tools. The cyclical structure of the model allowed students to move between experimentation, observation, theoretical understanding, and iterative design practice—thus fostering deep and reflective learning in a design studio setting.

Assessment through rubric-based evaluation

The rubric-based evaluation model has progressively evolved as a key tool for standardizing and objectifying performance assessment in educational contexts. One of the earliest documented examples is Hillegas’s scale for evaluating written expression. ²² This work was subsequently extended into a more systematic structure with the introduction of analytical rubrics by Diederich et al. ²³ Since the 1990s, rubrics have become widely adopted in educational practice, particularly with the rise of standards-based assessment frameworks, and are now commonly employed to evaluate complex, multi-criteria student outputs.

Rubrics provide students with clear performance criteria and actionable feedback for improvement, while offering educators a transparent and consistent framework for judgment. In design-based disciplines, rubrics are essential for evaluating creative outputs in a structured and accountable manner. Kasap and Kaptan emphasized the role of rubrics in ensuring objectivity and accountability in performance-based evaluation. ²⁴ Similarly, Utaberta et al. noted that the integration of rubrics into critique sessions in architectural education promotes more equitable and learning-focused environments. ²⁵

Ragheb argued that rubric-based assessments aligned with “assessment for learning” strategies foster deep learning by encouraging students to engage critically with their own work. ²⁶ Criterion-based evaluation models have been shown to enhance students’ creative problem-solving abilities and to support more effective management of the design process. ²⁶ Wolf and Komara further suggested that rubrics function not only as assessment instruments but also as tools for self-assessment, enabling students to understand and reflect upon their own learning trajectories. ²⁷

These studies collectively suggest that rubrics serve not merely as evaluative tools, but as pedagogical mechanisms that promote self-regulated learning, interdisciplinary integration, and iterative design thinking. Rubrics establish a common evaluative language between instructors and students, rendering assessment processes more interactive, transparent, and pedagogically aligned. When coupled with emerging technologies such as digital design platforms or augmented reality, rubrics can further enhance students’ ability to assess and refine their creative outputs, even in self-directed or hybrid learning environments.

These studies indicate that rubrics can be employed not only as evaluative instruments in architecture and interior architecture education but also as pedagogical mechanisms that support sustainability, foster innovative learning practices, and promote critical engagement with interdisciplinary design processes.

Rubric-based assessment typically consists of three core components:

(1) Performance Criteria – the specific knowledge, skills, or competencies being assessed,

(2) Performance Levels – the degree to which these criteria are achieved, and

(3) Descriptive Indicators – qualitative explanations that define each performance level.

In this study, rubric-based evaluation was conducted based on the following performance dimensions:

• the effective application of various knitting techniques and material explorations,

• creative form development supported by artificial intelligence,

• digital simulation and parametric design strategies, and

• the integration of computer-aided manufacturing -3D printing-technologies.

Performance levels were categorized on a five-point scale:

(1) – Inadequate,

(2) – Developing,

(3) – Moderately Successful,

(4) – Successful,

(5) – Highly Successful.

The detailed performance criteria, achievement levels, and their corresponding descriptors are presented in Table 1 below.

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Table 1.

Rubric framework: Performance criteria, achievement levels, and descriptive indicators.

Performance criteria	1	2	3	4	5
	Inadequate	Developing	Moderately successful	Successful	Highly successful
1. Experimentation with diverse materials	Limited understanding; materials poorly selected or applied	Basic application with limited variety of materials	Demonstrates awareness of material diversity; applies moderately well	Applies multiple techniques effectively and explores material behavior	Demonstrates advanced innovation and control through diverse material integration
2. Application of different knitting techniques	Lacks understanding of knitting techniques; poorly executed attempts	Basic application of one technique with errors or limited control	Demonstrates moderate ability to apply knitting techniques	Applies multiple knitting techniques effectively with design intent	Demonstrates mastery and creative integration of diverse knitting techniques
3. Integration of techniques and materials	No integration; techniques and materials applied separately without coherence	Minimal integration with weak connections between materials and techniques	Some integration achieved; demonstrates partial consistency	Effective integration of techniques and materials with design coherence	Advanced, seamless integration; demonstrates strong conceptual and material innovation
4. Use of AI-supported creative form-finding	No or minimal use of AI tools; lacks creative exploration	Uses AI tools but with limited variation or imagination	Generates creative outcomes with AI; moderate originality	Explores AI-generated variations effectively to inform design process	Synthesizes AI tools in highly original ways; demonstrates exceptional creativity and intent
5. Digital simulation and parametric design	No parametric logic applied; lacks clarity in digital modeling	Basic use of software; models are underdeveloped	Applies parametric relationships with some success	Develops coherent parametric models; demonstrates clear design logic	Creates complex, well-resolved parametric systems with strong formal and structural rationale
6. Computer-aided manufacturing (3D printing)	Lacks technical planning; 3D print unsuccessful or flawed	Basic 3D print with technical errors; low optimization	Functional 3D print; some understanding of fabrication parameters	Well-executed 3D prototype; good optimization of settings and material	Fully optimized, technically sound prototype with excellent detailing and fabrication strategy
7. Translation of knitting logic into spatial design	No evidence of translation from knitting to spatial design	Minimal translation; weak or unclear spatial outcomes	Partial translation; demonstrates some spatial potential	Effective translation of knitting logic into coherent spatial design	Highly innovative and original spatial design, fully derived from knitting logic

Learning styles based on student self-assessments

To determine students’ preferred learning styles, a structured self-assessment survey was administered in accordance with the four stages of Kolb’s experiential learning cycle: Concrete Experience (CE), Reflective Observation (RO), Abstract Conceptualization (AC), and Active Experimentation (AE). Each student evaluated their own skills in relation to the studio—particularly concerning the use of knitting techniques and digital tools—by rating themselves on eight sub-criteria using a 1–4 scale. The learning style associated with the highest cumulative score was identified as the dominant learning preference for that student.

The graph in Figure 13 illustrates the distribution of total scores for each student across the four learning stages. Key findings from the analysis are as follows:

• The Concrete Experience (CE) and Active Experimentation (AE) stages received the highest average scores across the cohort. This suggests that the studio, which emphasized hands-on making and iterative production, successfully supported practice-based and action-oriented learning.

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Figure 13.

Student scores by stage in Kolb’s experiential learning cycle.

Rubric-based performance analysis

Students’ performance throughout the studio was evaluated using a rubric-based framework aligned with the following seven performance criteria:

(1) Experimentation with Diverse Materials

Each student’s performance was scored based on these criteria using the five-point rubric scale previously defined. The average scores were calculated and visualized in Figure 14 to provide a comparative overview of individual strengths and areas needing improvement.OPEN IN VIEWER

The data visualization reveals nuanced differences in performance, enabling educators to identify where each student excelled and where further support may be beneficial. This level of individualized analysis demonstrates the utility of the rubric not only as a grading tool but also as a diagnostic instrument to enhance pedagogical responsiveness and support personalized learning trajectories.

Student performance was assessed based on specific evaluation criteria, including the use of diverse materials, application of digital tools, technical innovation, and creative output.

• Students ID2, ID5, and ID9 demonstrated the most balanced and consistently successful outcomes across all criteria.

The key findings derived from the rubric-based analysis are summarized below:

• High levels of performance were recorded in material experimentation and 3D printing applications. Students showcased both technical proficiency and creative exploration in these areas, suggesting strong engagement with hands-on fabrication and digital production tools.

Overall, the rubric-based performance analysis provided a transparent and structured framework to reveal both the strengths and developmental areas of individual students. This level of diagnostic insight underscores the value of rubric systems not only in assessing outcomes but also in shaping personalized and reflective learning processes within design education. Material-specific limitations also played a significant role in shaping student outcomes. For instance, cotton-dominant yarns provided dimensional stability but frequently led to brittle failures in 3D printing when scaled to larger lattice forms, whereas acrylic-based blends allowed greater curvature yet required additional support structures to prevent collapse. Several prototypes produced during the studio were unsuccessful due to filament breakage, excessive deformation, or incompatibility between knitted geometries and 3D printing tolerances. These failed attempts, however, were incorporated into the iterative learning process, enabling students to critically evaluate the constraints of material–structural translation. By explicitly addressing these limitations, the study demonstrates that the integration of knitting techniques was not merely conceptual but materially grounded and pedagogically valuable in highlighting both the potentials and boundaries of textile-informed design.

Group comparisons according to learning styles

After classifying the participants into three main groups (CE, RO, AE) according to their dominant learning styles, the average performances of each group were analyzed in terms of rubric criteria. Figure 15 graphically presents the comparative performance levels between these groups and makes the effects of learning style reflected on the design process visible.OPEN IN VIEWER

The findings suggest a meaningful correlation between Kolb’s learning styles and rubric-based performance outcomes. Specifically:

• Students categorized under the Concrete Experience (CE) group demonstrated strong performance in physical material experimentation, yet showed relatively limited engagement in digital simulation tasks.

These distinctions highlight the importance of designing diversified learning environments that accommodate a range of cognitive styles, particularly within pedagogical models that integrate analog and digital methods.

The average scores achieved by students across the rubric’s performance criteria were visualized using a radar chart, as shown in Figure 16. This visualization clearly illustrates that students who performed strongly in areas such as parametric design, 3D printing, and material experimentation emerged as leading performers. Conversely, the criterion related to AI-based design generation revealed more pronounced individual differences, suggesting that the integration of emerging technologies elicits varied levels of proficiency and comfort among learners.OPEN IN VIEWER

The radar chart visualizes students’ average performance across each rubric criterion. The data reveal that students such as ID2, ID5 and ID9 consistently demonstrated high and stable performance across all evaluation categories. An analysis of overall averages indicates that parametric design and 3D printing processes were among the most effectively executed components of the studio, while performance in the AI-assisted design criterion showed more variability, with some students underperforming in this area. These disparities may be attributed to differences in technical proficiency as well as varying levels of prior experience with digital tools. The radar visualization also made visible certain imbalances between assessment criteria and helped identify students’ strengths from a pedagogical perspective.

This study demonstrates that the integration of knitting techniques into architectural education offers multidimensional pedagogical and design-based contributions. The process was structured around embedding a traditional craft method—knitting—within a studio-based learning model through the use of digital design and fabrication technologies. Students engaged with knitting techniques both intuitively and analytically, using tools such as material prototyping, parametric modeling, AI-assisted variation generation, and 3D printing.

Key findings from the pedagogical analysis include:

• Pronounced individual differences were observed among students across multiple evaluation criteria.

Instead of general references to “innovation” or “creativity,” the study evidenced concrete learning gains. For example, rubric scores in Material Experimentation increased from an average of 3.1 to 4.5, while 3D Printing rose from 3.0 to 4.3. Similarly, 80% of students revised their initial prototypes after structured feedback sessions, demonstrating measurable improvement in iterative design ability. These results confirm that the pedagogical value of the studio was not merely aspirational but reflected in quantifiable progress.