Digital Transformation in Design Education: Exploring the Challenges and Opportunities in Jordanian Higher Education

Abstract

In recent years, design education has experienced major changes as the number of digital tools and technologies has rapidly developed. Many design programs encounter difficulties in integrating these innovations, despite their potential benefits. In this research, the adoption of digital tools in the teaching of design in Jordanian universities is explored, focusing on the views of educators in relation to their use, the challenges associated with it, and the resultant effects on the pedagogical process. Faculty members working in various departments of design were surveyed gauging the frequency of usage of tools, their knowledge of new technologies, their perceptions of the potential results of an educational process, and the barriers that were met during the integration process. To guide the analysis, three theoretical frameworks were applied: the SAMR model of technology integration, Bloom’s Digital Taxonomy, and the Technology Acceptance Model (TAM). The findings reveal that while traditional tools like AutoCAD and Revit are predominantly used at the Substitution and Augmentation stages, emerging technologies such as VR/AR and AI show potential for higher-order integration. However, barriers related to ease of use and perceived usefulness limit their broader adoption. The study contributes to the understanding of digital transformation in design education and provides insights into the pedagogical implications for future curriculum development. The research highlights the need to invest more in the professional development of educators and to work more closely with the technological industry. The proposed implications of these insights concern the restructuring of design education to reflect the needs of the digital age and provide approaches to overcoming obstacles to the successful adoption of technology in teaching environments.

1. Introduction

Due to the rapid pace of change in technology, the studies of design are experiencing significant change in the digital era [1]. The introduction of digital tools and platforms into design education has radically changed the practices and methods of design learning and practice [2,3]. Evidence shows that digital transformation has significant implications for higher education, and specifically in design fields, it introduces opportunities and challenges in relation to curriculum development, pedagogy, and student engagement [4]. Additional studies show that digital technologies and approaches can efficiently improve design education, thus equipping students to work in a more digital, mobile job industry [5]. The other prominent point of this development relates to the changing pedagogical behavior of teachers and the increased interest in digital means of teaching and learning. This pattern shows how traditional design teaching methods are evolving with the rise in digital technologies, with research examining their potential future effects on design education and the broader professional landscape [6]. A review of the modern tendencies in design education also highlights the influence of software, digital fabrication, and collaborative tools on educators and learners as digital tools start becoming part of design practice [7,8]. Though these global observations exist, there is still a lack of research, especially of a regional nature, particularly in the case of Jordan. Jordanian universities, which are progressively prioritizing integration of technology in education, have not comprehensively explored the impact of digital transformation on design education in the country. The current paper focused on filling this gap by performing a survey of teachers at Jordanian universities in a bid to understand their perceptions, challenges, and plans in terms of integrating digital tools in design education.

This paper uses three established theoretical frameworks to inform the analysis and provide a more in-depth theoretical insight. The degree of technological integration is assessed through the use of the SAMR model [9], which is a continuum of tools that started with simple substitution and ended with transformational change in education. These tools create cognitive levels ranging from basic knowledge retrieval to creative design, and are used to evaluate the level of cognitive engagement created by each tool using Bloom’s Digital Taxonomy [10]. Lastly, the Technology Acceptance Model (TAM) [11] is utilized to examine the factors that determine the adoption of these tools with specific reference to the sense of ease of use and usefulness. The adoption of those theoretical models grounds this study in relevance to Human–Computer Interaction (HCI) and digital pedagogy, and through the implementation of these frameworks—crucial for understanding how digital tools are not merely adopted but integrated into pedagogical practices—it examines their role in supporting active learning, user engagement, and creativity, core tenets of HCI and modern educational technology, while offering a detailed report on the usage of digital tools in design learning, which also illustrates a descriptive and critical overview of the current situation of digital transformation in Jordan.

2. Literature Review

Over the past few years, there has been a growing appeal for using digital technologies in design education, partly because of the need for both educators and learners to stay abreast with an increasingly dynamic educational environment [12]. Online technologies like design software and group work platforms have transformed teaching and practice of design and created new possibilities and problems in education and learning [13]. An increasing number of studies are investigating these changes as a valuable source of information on how digital technologies may either support or disrupt learning [5,14,15].

The use of digital tools has significantly influenced the field of design education by making the process of learning more flexible and interactive [16]. The impact of digital technologies on the field of design education is proven in the studies, which affirm that the tools create a more adaptable and flexible environment of learning [17,18]. Researchers argue that digital technologies have the potential to promote different types of learning, as well as deliver instant feedback, and promote remote teamwork and individual creativity, as well as group problem-solving skills [18]. However, it is recommended that they should be used sparingly: digital tools may be integrated to an extent that will lead to the stifling of critical design thinking [19].

Recent research also highlights the importance of interactive learning environments in fostering the engagement and creativity of students [20]. By using digital tools like software for 3D modeling and collaborative tools, students demonstrate improved interest in learning and experience active exchange during the design process. Also, they explore the digital tools that refine their creative output [21], which demonstrates the need to not only use digital tools but also make sure that the usage of those digital tools facilitates peer interaction and exploration.

Online resources have also been found to be effective in augmenting collaborative work, which is an essential part of design procedures [22]. Education using digital technologies enables collaborative learning, such as cloud-based tools and digital sketching tools that enable real-time feedback and collaboration, irrespective of geographic location [23]. This kind of partnership cannot be avoided in dealing with sophisticated design issues because different perspectives would lead to more innovative ideas. These technologies require the educators as well as the students to become skilled in online communication and teamwork.

Research into the adoption of digital design tools in architectural education within the fields of interior design, architecture, and urban design has compared the conventional learning environment to the digital learning environment with references to the use of computer-aided design software (CAD software), 3D modeling software, and technologies of digital fabrication, like 3D printing and computer numeric control machining [24,25]. Digital tools not only sped up and improved the design process, but they also gave students more freedom to be creative by letting them rapidly construct prototypes and try new things [26]. REVIT is mostly used in building information modeling (BIM)-based processes and is especially applicable in large, data-intensive architectural projects. The software also gives special emphasis on interdisciplinary coordination, thus making it very essential in producing construction documentation [27]. Rhino, in turn, is more focused on complex geometric lines and parametric design; hence, it can afford a greater degree of creative freedom. This is particularly relevant to areas requiring the development of freeform forms, like industrial design and architecture [28]. These technologies bring numerous advantages; nevertheless, they encounter some limitations, such as the high cost and difficulty of obtaining the right tools and the long process of learning that comes with understanding how to use complicated software.

Many studies regarding how digital fabrication changes the way people learn about design have been conducted. This study examined the potential of digital fabrication tools, such as 3D printers and CNC machines, in enhancing the process of design education [29]. The digital tools and technologies have significantly altered the process of prototyping, which enables students to swiftly create physical models of their designs, evaluate their concepts, and implement modifications with greater ease [30]. Students learn more about how things fit together in space and how materials work when they can quickly see and test design ideas in the real world. This bridges the gap between theoretical learning and practical application [31].

A significant progression in design education is the increasing application of virtual reality (VR) and immersive technologies, alongside their integration into design curricula, particularly in spatial design and architecture [32]. VR environments let students interact with their designs in very realistic ways, which helps them comprehend scale, space, and how components are related in space [33]. VR tools give students a chance to explore their designs in a virtual world [11], which makes learning more interactive and helps them understand and solve problems better [34]. However, it is also noted that the high cost of VR technology and the need for specialized training can make it hard for many people to use it [35].

Along with the technological tools themselves, digital platforms that make it easier to learn and work together online are becoming more common in design education [36]. The role of online platforms in design education, especially in remote learning settings, is that they have made design education easier to access by letting students from different places work together on projects [37]. While digital platforms do offer more freedom, they also make it harder for students to connect and feel like they belong to a community [38].

Finally, digital narration is becoming an increasingly popular way to teach design [39], particularly through the use of digital visual narrative in teaching design, especially in architecture and interior design classes [40]. Using technology to communicate helps students share their design ideas and also improves their narrative abilities and creative skills. Students learn to think critically about how design elements and communication work together when they make digital stories [41]. This is an important skill in many design fields [42], where the success of digital narratives relies on instructors’ capacity to navigate students through the process and offer constructive feedback [43,44].

These studies show that digital technologies have a complicated effect on design education. There are a lot of good things about using them, like more freedom, creativity, and teamwork. However, some problems need to be fixed. These include making sure that everyone has equal access to digital tools, establishing the right balance between creativity and technical skill, and figuring out how to manage digital collaboration well. As digital tools keep changing, we need to do more research to find out how they impact design education in the long run and how to use them in class best [45,46,47,48].

3. Materials and Methods

3.1. Research Design

This study employed a survey-based research design to examine the integration of digital tools in architecture and interior design education at Jordanian universities. The primary objective was to ascertain the utilization of these tools by educators, their proficiency with them, and their impact on students’ creativity and engagement in learning. The methodological structure of the study is summarized in Figure 1.

3.2. Survey Objectives

• Evaluation of the familiarity and frequency of use of various digital tools in design education.
• Determine how people think these tools will affect teaching and learning outcomes.
• Identify challenges in integrating these tools into curricula.
• Examine the role of emerging technologies (e.g., AI, VR) in design education.

3.3. Survey Structure and Tool Selection

The survey was structured around eight categories of digital tools commonly used in design education and as prevalent in relevant reviewed studies:

• CAD and BIM Software: Tools for architectural design and modeling (e.g., AutoCAD, Revit, Rhino).
• Parametric and Algorithmic Design Software: Tools for generative and algorithmic design (e.g., Grasshopper, Dynamo, Houdini).
• 3D Modeling and Rendering Tools: Tools for visualizing and rendering design concepts (e.g., Lumion, V-Ray, 3ds Max).
• Digital Fabrication and Prototyping Tools: Tools for creating physical prototypes (e.g., 3D printers, CNC machines).
• Virtual and Augmented Reality Tools: Tools for immersive and interactive design (e.g., Unreal Engine, Unity).
• Collaborative Design and Communication Tools: Tools for teamwork and project management (e.g., Miro, Figma, Google Drive).
• Image Editing and Post-Production Software: Tools for editing and presenting design visuals (e.g., Photoshop, Illustrator).
• AI Tools for Design: AI-driven tools for design generation and editing (e.g., Runway ML, DALL·E).

Even though eight categories of digital tools are assessed in this study, the wide range is not accidental. The target is to acquire an entire picture of the current adoption of various technologies, such as the CAD/BIM software, newly developed applications, such as VR/AR, and AI usage in the design education in Jordan. This approach to the methodology allows visualizing the entire picture of the digital transformation instead of focusing on the specific tools. The patterns of comparison that arise between these categories provide an understanding of the maturity of the adoption of digital and the trends in pedagogy that can be observed within design programs.

3.4. Survey Design and Implementation

The survey used closed-ended questions with a Likert scale to measure:

• Familiarity with the tools (Very Familiar, Somewhat Familiar, Not Familiar).
• Usage frequency (Daily, Weekly, Monthly, Rarely).
• Educational impact (Strongly Agree, Agree, Neutral, Disagree).
• Challenges (e.g., software cost, lack of training, limited access to computers).

A total of 100 educators from 20 universities (both private and public) offering architecture and interior design programs participated, resulting in a 75% response rate.

3.5. Data Analysis

Data were analyzed using descriptive statistics (means and percentages) to

• Identify the most commonly used tools across all categories.
• Assess familiarity with each tool.
• Evaluate the perceived educational impact of the tools.
• Identify the challenges educators face in implementing these tools in their teaching.

3.6. Statistical Analysis for Significance

To assess the significance of differences across tool categories, the following statistical tests were performed:

• Chi-Square Test of Independence: This test was used to assess whether there were significant differences in familiarity, usage frequency, and challenges across the eight tool categories.

  -

  Null Hypothesis (H₀): There is no significant difference across the categories.

  -

  Alternative Hypothesis (H₁): There is a significant difference across the categories.

  -

  Significance Level: A p-value < 0.05 was considered statistically significant, indicating a significant difference between tool categories.

2.

T-Test for Independent Samples: This test was used to compare the perceived educational impact between tool categories. It compared responses of “Strongly Agree” and “Agree” regarding educational impact, determining whether there were significant differences in perceptions of the tools’ educational value.

-

Null Hypothesis (H₀): There is no significant difference in the educational impact between tool categories.

-

Alternative Hypothesis (H₁): There is a significant difference in the educational impact between tool categories.

-

Significance Level: A p-value < 0.05 was used to assess statistical significance. If the p-value was below 0.05, the null hypothesis was rejected, indicating a significant difference in perceived educational impact across tool categories.

3.7. Data Collection and Grouping

The respondents were grouped based on

• Faculty experience: 50% junior faculty (0–5 years) and 50% senior faculty (6+ years).
• Discipline: 65% architecture faculty and 35% interior design faculty.

Furthermore, the data used in this study were fully anonymized and did not contain identifiable human subject information; therefore, informed consent was not required.

3.8. Statistical Software

Data analysis was conducted using SPSS (Statistical Package for the Social Sciences, version 30). The Chi-Square and T-tests were chosen due to their ability to analyze categorical and continuous data, respectively. For both tests, the threshold for statistical significance was set at a p-value < 0.05.

3.9. Applying Widely Recognized Theoretical Frameworks

This study uses three theoretical frameworks, i.e., SAMR, Bloom’s Digital Taxonomy, and Technology Acceptance Model (TAM), to understand the integration of digital tools in design education. A detailed description of the operationalization of each framework in the context of the present investigation follows below.

• The SAMR model (Substitution, Augmentation, Modification, and Redefinition) will be used to evaluate the level of technological integration that was demonstrated by the digital tools that were used for design education.

  -

  Procedure: Survey data will be analyzed, and each digital tool will be classified by one of the four SAMR stages. This classification will be based on the educators’ reports on the usage of the tools in their practices.

  -

  Substitution: Tools that replace conventional methods but do not change the education experience much.

  -

  Augmentation: Tools that offer improvements over traditional methods and, thus, functional enhancements that are added to the learning process.

  -

  Modification: Tools that allow for a great deal of redesign of learning tasks, thus enabling more creative and complex design work.

  -

  Redefinition: Tools that give rise to new tasks that are previously inconceivable and, thus, provide transformative learning experiences.

b.

Bloom’s Digital Taxonomy will be used to assess cognitive engagement promoted in each digital tool. The taxonomy outlines a hierarchy of cognitive skills from the lower-order to the higher-order thinking.

-

Procedure: For each tool, data will be surveyed to determine which cognitive levels are supported the most. Tools will be evaluated based on the types of tasks that they will allow students to do.

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Lower-order cognitive tasks, such as Remembering and Applying, will be associated with tools that support basic design functions (e.g., AutoCAD).

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Higher-order cognitive tasks that will be linked to tools that foster creativity and increased engagement with the design process (e.g., VR/AR).

c.

The Technology Acceptance Model will be used for the evaluation of perceived ease of use and perceived usefulness of each digital tool, as these perceptions impact the propensity to use the tools in educational settings.

-

Procedure: Respondents in the survey will be asked to rate each tool in terms of perceived ease of use and perceived usefulness. These ratings will then be analyzed to see which attributes—ease of use, usefulness, or both—are most important for adoption.

-

Perceived Ease of Use: How easy the educators feel the tool is to use, with little effort or special training required.

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Perceived Usefulness: How much educators perceived that the use of the tool increased the effectiveness of teaching and student learning outcomes [9,10,11].

4. Results

The survey was completed by 100 respondents from 20 universities (both private and governmental) offering architecture and interior design programs. The data provides insights into how these tools are perceived and used in design education in Jordan. Taking into account that the number of universities contributing is 20 (13 with Architecture departments, 7 with Interior Design departments, and some universities offering both), the number of respondents per university was 5 respondents, bringing the total respondents participating in the study from the 20 universities to 100 respondents.

4.1. Data Analysis Results

The descriptive analysis Results Section is presented with the following structure for each of the eight categories:

• Familiarity: Present the familiarity data, followed by the chart and its interpretation.
• Usage Frequency: Present the usage frequency data, followed by the chart and its interpretation.
• Educational Impact: Present the educational impact data, followed by the chart and its interpretation.
• Challenges: Present the challenges data, followed by the chart and its interpretation.

4.1.1. Results Section for the First Category; CAD/BIM Tools

A.

Familiarity with CAD/BIM Tools, as shown in

Table 1. Familiarity with CAD/BIM tools (authors).

Tool	Very Familiar (%)	Somewhat Familiar (%)	Not Familiar (%)
AutoCAD	80	15	5
Revit	65	25	10
Rhino	40	35	25
Archicad	55	30	15
SketchUp	75	20	5

.

Results from this analysis show that AutoCAD and SketchUp have the highest familiarity, with 80% and 75% of respondents being very familiar with them, respectively. This indicates that these tools are well-established and commonly used in design education. While Revit and Archicad have lower familiarity levels (65% and 55%), suggesting that these tools are important but less universally recognized or adopted compared to AutoCAD. And finally, Rhino shows the lowest level of familiarity, with only 40% of respondents reporting high familiarity, possibly reflecting its more specialized use.

B.

Usage Frequency of CAD/BIM Tools, as shown in

Table 2. Usage frequency of CAD/BIM tools (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
AutoCAD	50	30	15	5
Revit	25	40	25	10
Rhino	10	25	40	25
Archicad	30	35	20	15
SketchUp	60	25	10	5

.

Results from this analysis show that SketchUp has the highest daily usage (60%), followed by AutoCAD (50%). These tools are used most frequently, suggesting their importance in daily design tasks. Revit shows moderate usage, with 25% of respondents using it daily and 40% using it weekly. This reflects its importance but indicates it may not be integrated into daily teaching. Meanwhile, Rhino and Archicad are used less frequently, with only 10% of respondents using Rhino daily. These tools are likely used for more specialized tasks and may require additional training or resources.

C.

Educational Impact of CAD/BIM Tools, as shown in

Table 3. Educational impact of CAD/BIM tools (authors).

Tool	Strongly Agree (%)	Agree (%)	Neutral (%)	Disagree (%)
AutoCAD	70	20	10	0
Revit	40	35	15	10
Rhino	20	30	40	10
Archicad	50	30	15	5
SketchUp	80	15	5	0

.

Results from this analysis show that SketchUp has the highest positive impact, with 80% of educators strongly agreeing that it enhances student learning. Its ease of use and visualization capabilities make it an essential tool in design education. AutoCAD also shows a strong impact, with 70% strongly agreeing that it improves teaching and learning. Revit has a more mixed impact, with only 40% strongly agreeing. Given its complexity, educators may need more training to integrate it into teaching fully. And finally, Rhino shows a lower educational impact, with only 20% strongly agreeing. This could be due to its specialized nature and higher learning curve.

D.

Challenges in Using CAD/BIM Tools, as shown in

Table 4. Challenges in using CAD/BIM tools (authors).

Challenge	AutoCAD (%)	Revit (%)	Rhino (%)	Archicad (%)	SketchUp (%)
High Software Cost	60	60	60	50	40
Lack of Training or Expertise	55	55	50	60	35
Limited Access to Computers	45	45	45	40	30
Technical Difficulties	30	30	35	25	20

.

Results from this analysis show that the most significant challenge reported for all tools is the high software cost (60%). This reflects the expensive licensing for professional design tools, which may limit access in educational institutions. Lack of training and limited access to computers were also significant barriers, especially for tools like Revit and Rhino that require specialized hardware and expertise. Technical difficulties were less common but still notable for tools like Rhino (35%) and Revit (30%), possibly indicating the complexity of these tools.

The following are the charts corresponding to the descriptive analysis for the Results Section for the first category, CAD/BIM tools, as shown in Figure 2.

4.1.2. Results Section for the Second Category; Parametric and Algorithmic Design Software

A.

Familiarity with Parametric and Algorithmic Design Software, as shown in

Table 5. Familiarity with Parametric and Algorithmic Design Software (authors).

Tool	Very Familiar (%)	Somewhat Familiar (%)	Not Familiar (%)
Grasshopper (Rhino)	70	20	10
Dynamo (Revit)	50	35	15
Houdini	30	40	30
TouchDesigner	20	50	30

.

Results from this analysis show that Grasshopper (for Rhino) has the highest familiarity, with 70% of respondents being very familiar with it. This is consistent with its widespread use in architectural and design education, especially in programs that focus on parametric design. Dynamo (for Revit) shows a moderate level of familiarity, with 50% of respondents reporting that they are very familiar with it. Dynamo is used in BIM workflows but is not as universally known as Grasshopper, reflecting its specialized application. Houdini and TouchDesigner have lower familiarity levels, with 30% and 20% of respondents being very familiar with them, respectively. These tools are more specialized for advanced design and animation, which may explain their lower adoption rate in standard design curricula.

B.

Usage Frequency of Parametric and Algorithmic Design Software, as shown in

Table 6. Usage frequency of Parametric and Algorithmic Design Software (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
Grasshopper (Rhino)	40	30	20	10
Dynamo (Revit)	20	40	30	10
Houdini	10	30	40	20
TouchDesigner	5	20	50	25

.

Results from this analysis show that Grasshopper is the most commonly used tool on a daily basis, with 40% of respondents using it regularly. This highlights Grasshopper’s importance in design education, particularly in courses focused on parametric design and computational design methods. Dynamo is used more moderately, with 20% of respondents using it daily. This suggests that while Dynamo is important in BIM environments, it may not be as frequently used as Grasshopper in non-BIM-focused design curricula. Houdini and TouchDesigner have much lower daily usage, with 10% and 5% of respondents using them daily, respectively. These tools are likely reserved for more advanced, specialized design courses and are not yet integrated into the core curriculum for most design programs.

C.

Educational Impact of Parametric and Algorithmic Design Software, as shown in

Table 7. Educational impact of Parametric and Algorithmic Design Software (authors).

Tool	Strongly Agree (%)	Agree (%)	Neutral (%)	Disagree (%)
Grasshopper (Rhino)	60	30	10	0
Dynamo (Revit)	40	40	15	5
Houdini	25	40	25	10
TouchDesigner	15	45	30	10

.

Results from this analysis show that Grasshopper shows the strongest educational impact, with 60% of respondents strongly agreeing that it enhances teaching and learning. Its integration with Rhino allows for rapid design exploration and facilitates computational thinking, making it a powerful educational tool in design studios. Dynamo also shows a positive impact, with 40% of respondents strongly agreeing that it improves education. As a tool for integrating design and BIM workflows, it has a strong educational impact, particularly for students learning BIM processes. Houdini and TouchDesigner have lower perceived educational impact, with only 25% and 15% strongly agreeing, respectively. While these tools are powerful for 3D modeling and animation, they are more specialized and may not have widespread educational application in general design programs.

D.

Challenges in Using Parametric and Algorithmic Design Software, as shown in

Table 8. Challenges in using Parametric and Algorithmic Design Software (authors).

Challenge	Grasshopper (%)	Dynamo (%)	Houdini (%)	TouchDesigner (%)
High Software Cost	60	60	55	50
Lack of Training	55	55	60	50
Limited Access to Computers	45	45	40	30
Technical Difficulties	40	40	45	35

.

Results from this analysis shows that high software cost is the most common challenge, reported by 60% of respondents across all tools. This highlights the barrier that the high cost of professional-grade software (like Rhino, Revit, Houdini, etc.) can have in limiting access to these tools, especially in institutions with constrained budgets. Lack of training is the second most common challenge, with 55% of respondents citing it, particularly for more advanced tools like Houdini and TouchDesigner. This indicates that even when tools are available, educators may lack the resources or expertise to teach them effectively. Limited access to computers is also an important challenge, reported by 45% of respondents, reflecting the fact that some of these tools require significant computational power, which may not be available in all institutions. Technical difficulties are reported by 40% of respondents, particularly for TouchDesigner and Houdini, which may be more complex to use and integrate into educational curricula.

The following are the charts corresponding to the descriptive analysis for the Results Section for the Second Category, Parametric and Algorithmic Design Software, as shown in Figure 3.

4.1.3. Results Section for the Third Category; 3D Modeling and Rendering Tools

A.

Familiarity with 3D Modeling and Rendering Tools, as shown in

Table 9. Familiarity with 3D Modeling and Rendering Tools (authors).

Tool	Very Familiar %	Somewhat Familiar %	Not Familiar %
Lumion	75	20	5
Twinmotion	70	25	5
3ds Max	65	30	5
V-Ray	80	15	5
Corona Renderer	60	35	5

.

Results from this analysis show that V-Ray is the most familiar tool, with 80% of respondents reporting being very familiar with it. This reflects its widespread use in design education and industry for rendering purposes. Lumion and Twinmotion also have high familiarity rates, with 75% and 70% of respondents being very familiar, respectively. These tools are popular for their user-friendly interfaces and real-time rendering capabilities. 3ds Max shows moderate familiarity, with 65% reporting a high level of familiarity. While it is widely used in professional design settings, it may be less commonly incorporated into design education compared to tools like V-Ray. Corona Renderer has the lowest familiarity at 60%, though still relatively high. Its use in educational settings may be more recent or less prevalent than the other tools.

B.

Usage Frequency of 3D Modeling and Rendering Tools, as shown in

Table 10. Usage frequency of 3D Modeling and Rendering Tools (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
Lumion	40	35	20	5
Twinmotion	35	40	20	5
3ds Max	25	45	25	5
V-Ray	30	40	20	10
Corona Renderer	20	50	25	5

.

The analysis shows that Lumion and Twinmotion have the highest percentage of daily usage, with 40 percent and 35 percent of the respondents, respectively, indicating that they use the tools daily. They are effective because of their fast-processing speeds and user-friendly interfaces, which ensure that realistic visualizations are produced. Three-de-eight-seo (3ds 3Ds Max) shows moderate usage or 25 percent of respondents; its relatively complex nature could restrict its ease of use in everyday classroom use by some individuals. The frequency of V-Ray and Corona Renderer is utilized at a lower rate on a daily basis, with the daily use reported at 30, indicating that people use it daily, and 20 say that they use it daily. These are also highly specialized applications, with the workflow of producing high-quality rendering often requiring more time and computation capacity to produce detailed visualization.

C.

Educational Impact of 3D Modeling and Rendering Tools, as shown in

Table 11. Educational impact of 3D Modelling and Rendering Tools (authors).

Tool	Strongly Agree (%)	Agree (%)	Neutral (%)	Disagree (%)
Lumion	70	25	5	0
Twinmotion	65	30	5	0
3ds Max	50	40	10	0
V-Ray	75	15	10	0
Corona Renderer	60	35	5	0

.

The same analytical framework reveals that V-Ray has an outstanding educational presence, with 75 percent of the respondents providing a high recommendation of effectiveness in the promotion of teaching and learning. The high fidelity and versatility of the tool make it a great asset in the design pedagogy. Lumion and Twinmotion also exhibit a high positive impact, with 70 percent and 65 percent of teachers confirming that they contribute to student learning, respectively. Their ability to provide real-time visualizations is particularly useful in enabling them to perform quick design iterations. Three-de-e-eight-seo is moderately positive; 50 percent of the respondents claim to agree with it strongly, but its complexity might require further training and practice, yet it provides advanced functionality to build complex 3D constructions. Corona Renderer has slightly less impact when compared to V-Ray, but is still positive, where 60 percent strongly agreed that it maximizes educational output. The high-quality design communication is supported by the photorealism of the tool.

D.

Challenges in Using 3D Modeling and Rendering Tools, as shown in

Table 12. Challenges in using 3D Modeling and Rendering Tools (authors).

Challenge	Lumion %	Twinmotion %	3ds Max %	V-Ray %	Corona Renderer %
High Software Cost	50	45	60	55	50
Lack of Training	40	40	45	35	45
Limited Access to Computers	30	35	40	30	35
Technical Difficulties	25	30	35	40	30

.

Results from this analysis show that high software cost is the most significant challenge across all tools, with 60% of respondents reporting this challenge for 3ds Max. The high cost of professional software licenses is a common barrier to their widespread use in educational settings. Lack of training is also a notable challenge, particularly for tools like 3ds Max and Corona Renderer, where 45% of respondents report this barrier. This highlights the need for professional development and training to integrate these tools into curricula effectively. Limited access to computers is a challenge, especially for tools that require high-performance hardware, such as 3ds Max and V-Ray. Technical difficulties were less common but still notable, particularly for V-Ray, where 40% of educators reported challenges in handling the tool’s complex settings. The following are the charts corresponding to the descriptive analysis for the Results Section for the Third Category: 3D Modeling and Rendering Tools, as shown in the charts in Figure 4.

4.1.4. Results Section for the Fourth Category; Digital Fabrication and Prototyping Tools

A.

Familiarity with Digital Fabrication and Prototyping Tools, as shown in

Table 13. Familiarity with Digital Fabrication and Prototyping Tools (authors).

Tool	Very Familiar %	Somewhat Familiar %	Not Familiar %
Laser Cutters	60	30	10
CNC Milling Machines	50	35	15
3D Printers	75	15	10
Router Machines	65	25	10

.

Regarding familiarity, the 3D printers have the most recognition, with 75% of respondents indicating high familiarity. This tool is widely used in academic and professional environments to prototype quickly and is becoming a part of design courses. The moderate familiarity is reached by laser cutters and router machines, which have the reported levels of 60 and 65%, respectively; these tools are predominantly used in the production of precise and detailed models, albeit with specialized knowledge. CNC milling machines are the least familiar, with 50% of the participants reporting a high familiarity. Although they are common in industrial and architectural fabrication, they are also not as commonly used in design education programs as 3D printers.

B.

Usage Frequency of Digital Fabrication and Prototyping Tools, as shown in

Table 14. Usage frequency of Digital Fabrication and Prototyping Tools (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
Laser Cutters	40	30	20	10
CNC Milling Machines	35	30	25	10
3D Printers	50	30	15	5
Router Machines	45	25	20	10

.

Daily use analysis shows that 3D printers are the most used, with 50% of the respondents reporting daily usage. This trend indicates increased accessibility and teaching integration of 3D printing in design education for rapid prototyping. Laser cutters and router machines also demonstrate moderate usage with 40% and 45 percent daily use, respectively; these tools are necessary to make precise fabrication but are not used as often as 3D printers. CNC milling machines show the lowest usage per day, with 35 percent of the participants using the machines weekly, which indicates that their application is more associated with sophisticated fabrication operations than everyday teaching.

C.

Educational Impact of Digital Fabrication and Prototyping Tools, as shown in

Table 15. Educational impact of Digital Fabrication and Prototyping Tools (authors).

Tool	Strongly Agree %	Agree %	Neutral %	Disagree %
Laser Cutters	75	20	5	0
CNC Milling Machines	60	30	10	0
3D Printers	85	10	5	0
Router Machines	70	20	10	0

.

The last evaluation of educational impact signifies that the role of 3D printers has the strongest impact, with 85% of the respondents strongly supporting this as a tool to enhance teaching and learning. This was influenced by their versatility and continued spread to design education. Laser cutters and router machines also exhibit a positive effect, with 75% and 70% of respondents saying that the tools have improved learning outcomes, respectively. These tools are essential for accuracy work and may significantly enliven practical designing experiences. The influence of CNC milling machines is somewhat more uneven, with 60% of the respondents strongly agreeing that the machines might help in enhancing education, reflecting a more specialized use in the wider education spectrum.

D.

Challenges in Using Digital Fabrication and Prototyping Tools, as shown in

Table 16. Challenges in using Digital Fabrication and Prototyping Tools(authors).

Challenge	Laser Cutters %	CNC Milling Machines %	3D Printers %	Router Machines %
High Software Cost	60	55	50	50
Lack of Training	55	50	45	50
Limited Access to Computers	45	40	35	40
Technical Difficulties	40	45	35	30

.

The results show that high software cost is the main bottleneck in all digital fabrication tools, with laser cutters (60%) and CNC milling machines (55%) being the most vulnerable. Next in line is the lack of training, especially among laser cutters (55%) and CNC milling machines (50%), which demonstrates the need to have specific knowledge. Limited access to computing facilities affects all devices, but with a significant effect on 3D printers (35%), this further intensifies the need to have high-performance hardware. Although the technical complications are less common, they are still consequential, especially the router machines (30%), with the lowest rate of occurrence. The leading barriers that hinder the successful implementation of these technologies into design pedagogy include cost, training, and availability of resources, collectively.

The following are the charts corresponding to the descriptive analysis for the Results Section for the Fourth Category, Digital Fabrication and Prototyping Tools, as shown in Figure 5.

4.1.5. Results Section for the Fifth Category; Virtual and Augmented Reality Tools

A.

Familiarity with Virtual and Augmented Reality Tools, as shown in

Table 17. Familiarity with Virtual and Augmented Reality Tools (authors).

Tool	Very Familiar %	Somewhat Familiar %	Not Familiar %
Unreal Engine	60	30	10
Unity	55	35	10
Enscape	70	20	10
IrisVR	40	40	20
Hololens	30	30	40

.

Results from this analysis show that Enscape is the most familiar tool, with 70% of respondents being very familiar with it. It is widely used in design education for immersive visualizations. Unreal Engine and Unity also have a strong familiarity rate of 60% and 55%, respectively. These tools are well-known for their interactive and immersive capabilities in design education. IrisVR and Hololens have lower familiarity levels, with 40% and 30% of respondents being very familiar. These tools are more specialized and may not yet be integrated into the core curricula of many design programs.

B.

Usage Frequency of Virtual and Augmented Reality Tools, as shown in

Table 18. Usage frequency of Virtual and Augmented Reality Tools (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
Unreal Engine	40	35	20	5
Unity	35	40	20	5
Enscape	45	35	15	5
IrisVR	25	45	25	5
Hololens	20	40	30	10

.

Results from this analysis show that Enscape has the highest daily usage at 45%, followed by Unreal Engine and Unity at 40% and 35%, respectively. These tools are frequently used in design studios for immersive visualizations. IrisVR and Hololens are used less frequently, with 25% and 20% using them daily, indicating that these tools might be more specialized or used in specific courses. IrisVR and Hololens show relatively high weekly usage, with 45% and 40% using them regularly, indicating that they are still being integrated into the curriculum but with moderate frequency.

C.

Educational Impact of Virtual and Augmented Reality Tools, as shown in

Table 19. Educational Impact of Virtual and Augmented Reality Tools (authors).

Tool	Strongly Agree (%)	Agree (%)	Neutral (%)	Disagree (%)
Unreal Engine	50	30	15	5
Unity	45	40	10	5
Enscape	60	25	10	5
IrisVR	35	40	20	5
Hololens	30	35	25	10

.

Results from this analysis show that Enscape has the highest positive impact, with 60% of educators strongly agreeing that it enhances teaching and learning. Its real-time rendering capabilities make it a powerful tool for design education. Unreal Engine and Unity also show strong educational impact, with 50% and 45% of educators strongly agreeing. These tools support immersive and interactive learning experiences. IrisVR and Hololens show a more mixed impact, with 35% and 30% strongly agreeing. While these tools are impactful for certain educational contexts, their specialized nature might limit their widespread application.

D.

Challenges in Using Virtual and Augmented Reality Tools, as shown in

Table 20. Challenges in Using Virtual and Augmented Reality Tools (authors).

Challenge	Unreal Engine (%)	Unity (%)	Enscape (%)	IrisVR (%)	Hololens (%)
High Software Cost	55	50	45	60	65
Lack of Training	45	40	50	45	40
Limited Access to Computers	40	35	30	40	50
Technical Difficulties	35	40	35	30	25

.

Results from this analysis show that high software cost is the most significant challenge across all tools, with 65% of respondents citing it for Hololens and 60% for IrisVR. The high cost of VR/AR hardware and software licenses is a common barrier in educational settings. Lack of training is also a major issue, with 50% of respondents citing it for Enscape. This suggests a need for more professional development and resources to fully integrate these tools into design education. Limited access to computers is a significant challenge, particularly for Hololens, with 50% of respondents indicating this as a barrier. VR/AR tools often require high-performance computers or specialized hardware, which may not be available in all institutions. Technical difficulties were reported by 35% of respondents for Unreal Engine and Unity, indicating that while these tools offer great potential, they may require more technical expertise to integrate into the curriculum effectively.

The following are the charts corresponding to the descriptive analysis for the Results Section for the Fifth Category, Virtual and Augmented Reality Tools, as shown in Figure 6.

4.1.6. Results Section for the Sixth Category; Image Editing and Post-Production Tools

A.

Familiarity with Image Editing and Post-Production Software, as shown in

Table 21. Familiarity with Image Editing and Post-Production Software (authors).

Tool	Very Familiar %	Somewhat Familiar %	Not Familiar %
Adobe Photoshop	85	10	5
Adobe Illustrator	80	15	5
CorelDRAW	60	20	20
GIMP	50	30	20
InDesign	70	15	15

.

Results from this analysis show that Adobe Photoshop is the most familiar tool, with 85% of respondents being very familiar with it. This reflects its widespread use in both professional design and educational settings for editing and enhancing visual content. Adobe Illustrator follows closely, with 80% of respondents being very familiar, confirming its significant role in vector graphic design. InDesign also shows high familiarity at 70%, as it is essential for layout design and final presentation work. CorelDRAW and GIMP have lower familiarity rates, with 60% and 50%, respectively, but still indicate solid usage in certain educational contexts, particularly in open-source environments (GIMP) and in traditional design settings (CorelDRAW).

B.

Usage frequency of Image Editing and Post-Production Software, as shown in

Table 22. Usage frequency of Image Editing and Post-Production Software (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
Adobe Photoshop	70	25	5	0
Adobe Illustrator	60	30	5	5
CorelDRAW	25	40	20	15
GIMP	15	40	30	15
InDesign	50	35	10	5

.

The results of this survey show that 70% of the people who answered use Adobe Photoshop every day. This shows that it is an important part of design education for things like editing images, changing the way they look, and making them look better. Adobe Illustrator is also used a lot every day, at 60%, which makes sense because it is important for making designs and illustrations. Half of the people who answered said they use InDesign every day, which shows how important it is for layout and presentation design. CorelDRAW and GIMP are used less often every day, with 25% and 15% of users, respectively. However, both tools are still used a lot, especially in some specialized design courses or for open-source projects.

C.

Educational Impact of Image Editing and Post-Production Software, as shown in

Table 23. Educational Impact of Image Editing and Post-Production Software (authors).

Tool	Strongly Agree (%)	Agree (%)	Neutral (%)	Disagree (%)
Adobe Photoshop	85	10	5	0
Adobe Illustrator	80	15	5	0
CorelDRAW	60	30	10	0
GIMP	55	35	10	0
InDesign	75	20	5	0

.

Results from this analysis show that Adobe Photoshop has the highest educational impact, with 85% of educators strongly agreeing that it enhances teaching and learning. Its role in image manipulation, retouching, and creating design assets makes it indispensable in design education. Adobe Illustrator follows closely, with 80% of respondents strongly agreeing about its positive impact, highlighting its effectiveness in vector-based graphic design. InDesign also has a strong educational impact, with 75% of respondents strongly agreeing that it helps in layout design and final presentation work. CorelDRAW and GIMP show moderate educational impact, with 60% and 55% strongly agreeing, indicating their relevance in some design curricula, particularly in more traditional or open-source contexts.

D.

Challenges in Using Image Editing and Post-Production Software, as shown in

Table 24. Challenges in Using Image Editing and Post-Production Software (authors).

Challenge	Adobe Photoshop %	Adobe Illustrator %	CorelDRAW %	GIMP %	InDesign %
High Software Cost	60	55	50	40	45
Lack of Training	55	50	45	50	40
Limited Access to Computers	40	35	40	35	30
Technical Difficulties	25	30	35	30	20

.

Results from this analysis show that high software cost is the most significant challenge for all tools, particularly for Adobe Photoshop and Illustrator, with 60% and 55% of respondents reporting it. The high cost of licensing for these industry-standard tools can be a barrier in educational environments, especially in institutions with limited budgets. Lack of training is a notable issue, reported by 55% of respondents for Photoshop and 50% for Illustrator, indicating that educators may need more professional development or resources to teach these tools effectively. Limited access to computers is a challenge for all tools, particularly for Photoshop, Illustrator, and CorelDRAW, where 40% to 60% of respondents reported this issue. These tools often require high-performance computers, which may not be available in all institutions. Technical difficulties were less common but still notable, especially for CorelDRAW and GIMP, where 35% and 30% of respondents cited this barrier. These issues may be related to compatibility or software setup.

The following are the charts corresponding to the descriptive analysis for the Results Section for the Sixth Category, Image Editing and Post-Production Tools, as shown in Figure 7.

4.1.7. Results Section for the Seventh Category; Collaborative Design and Communication Tools

A.

Familiarity with Collaborative Design and Communication Tools, as shown in

Table 25. Familiarity with Collaborative Design and Communication Tools (authors).

Tool	Very Familiar (%)	Somewhat Familiar (%)	Not Familiar (%)
Miro	65	25	10
Figma	60	30	10
Trello	70	20	10
Slack	55	35	10
Google Drive	80	15	5

.

The familiarity data show a relatively high recognition of Google Drive (80%) and Trello (70%) as well-established tools in design education. In contrast, tools like Figma and Slack are moderately familiar but not as universally used.

B.

Usage frequency of Collaborative Design and Communication Tools, as shown in

Table 26. Usage frequency of Collaborative Design and Communication Tools (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
Miro	35	40	20	5
Figma	30	50	15	5
Trello	50	35	10	5
Slack	40	45	10	5
Google Drive	60	30	10	0

.

The usage frequency data indicate that Google Drive is used most frequently (60% daily), followed by Trello (50%). However, tools like Miro and Figma are used less frequently.

C.

Educational Impact of Collaborative Design and Communication Tools, as shown in

Table 27. Educational Impact of Collaborative Design and Communication Tools (authors).

Tool	Strongly Agree (%)	Agree (%)	Neutral (%)	Disagree (%)
Miro	60	35	5	0
Figma	55	40	5	0
Trello	65	30	5	0
Slack	50	40	10	0
Google Drive	75	20	5	0

.

The educational impact data show a positive effect across all tools, with Google Drive and Trello leading in strong agreement for enhancing teaching and learning.

D.

Challenges in Using Collaborative Design and Communication Tools, as shown in

Table 28. Challenges in Using Collaborative Design and Communication Tools (authors).

Challenge	Miro (%)	Figma (%)	Trello (%)	Slack (%)	Google Drive (%)
High Software Cost	30	40	30	25	20
Lack of Training	40	35	40	30	25
Limited Access to Computers	25	20	25	20	15
Technical Difficulties	20	25	20	15	10

.

The most common challenge faced across all tools is a lack of training, followed by high software costs. The following are the charts corresponding to the descriptive analysis for the Results Section for the Seventh Category, Collaborative Design and Communication Tools, as shown in Figure 8.

4.1.8. Results Section for the Eighth Category: AI Tools for Design Generation, Editing, and Automation Tools

A.

Familiarity with AI Tools for Design Generation, Editing, and Automation, as shown in

Table 29. Familiarity with AI Tools for Design Generation, Editing, and Automation (authors).

Tool	Very Familiar %	Somewhat Familiar %	Not Familiar (%)
Runway ML	25	35	40
DALL·E	30	40	30
Fusion 360 Generative Design	20	45	35
Spacemaker AI	15	50	35
Artbreeder	10	60	30

.

Results from this analysis show that DALL·E has the highest familiarity, with 30% of respondents being very familiar with it. As an AI tool focused on generating images from text descriptions, it has made a significant impact in creative fields. Runway ML follows closely with 25% familiarity, indicating its growing presence in design education for AI-driven design generation. Fusion 360 Generative Design has 20% familiarity, which reflects its more specialized application in engineering and architecture design. Spacemaker AI and Artbreeder have lower familiarity rates, with 15% and 10%, respectively, indicating that these tools are still relatively niche in the design education sector.

B.

Usage Frequency of AI Tools for Design Generation, Editing, and Automation, as shown in

Table 30. Usage frequency of AI Tools (authors).

Tool	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)
Runway ML	5	20	35	40
DALL·E	5	15	30	50
Fusion 360 Generative Design	10	25	40	25
Spacemaker AI	5	15	45	35
Artbreeder	5	25	50	20

.

Results from this analysis show that Fusion 360 Generative Design has the highest daily usage at 10%, suggesting it is utilized in more specialized courses focusing on generative design and parametric modeling. Runway ML and DALL·E are used more on a monthly or rarely basis, with 35% and 30% of respondents using them monthly, indicating that while these tools are seen as valuable, they may not yet be integrated into day-to-day curricula. Spacemaker AI and Artbreeder show lower daily usage, with only 5% using them regularly, but both tools are utilized in more advanced or experimental contexts.

C.

Educational Impact of AI Tools for Design Generation, Editing, and Automation, as shown in

Table 31. Educational impact of AI Tools (authors).

Tool	Strongly Agree %	Agree %	Neutral %	Disagree %
Runway ML	20	40	30	10
DALL·E	25	35	35	5
Fusion 360 Generative Design	20	40	30	10
Spacemaker AI	15	45	30	10
Artbreeder	10	50	30	10

.

Results from this analysis show that DALL·E has the strongest perceived educational impact, with 25% of educators strongly agreeing that it enhances teaching and learning, particularly in creative and conceptual design contexts. Runway ML also shows a positive impact, with 20% of respondents strongly agreeing that it improves education. As an AI platform for creative applications, it has a unique role in generating design ideas. Fusion 360 Generative Design and Spacemaker AI show a moderate impact, with 20% of educators strongly agreeing on their educational value. These tools are highly specialized and used for specific applications in generative and architectural design. Artbreeder has the lowest educational impact but still shows some potential, with 10% of educators strongly agreeing about its usefulness in design education.

D.

Challenges in Using AI Tools for Design Generation, Editing, and Automation, as shown in

Table 32. Challenges in using AI Tools for Design Generation, Editing, and Automation (authors).

Challenge	Runway ML %	DALL·E %	Fusion 360 Generative Design %	Spacemaker AI %	Artbreeder %
High Software Cost	60	55	50	65	55
Lack of Training	50	45	55	50	45
Limited Access to Computers	40	35	45	40	40
Technical Difficulties	35	40	35	30	30

.

Results from this analysis show that high software cost is the primary challenge for all tools, with 65% of respondents reporting it for Spacemaker AI and 60% for Runway ML. These tools require expensive licenses or high-performance hardware, which may not be available in all institutions. Lack of training is also a common issue, especially for tools like Fusion 360 Generative Design and Runway ML, with 50% and 55% of respondents highlighting the need for more resources to teach these tools effectively. Limited access to computers is another barrier, particularly for more resource-intensive tools like Spacemaker AI and Fusion 360 Generative Design, where high-performance hardware is required. Technical difficulties are a moderate concern, with 35% of respondents reporting challenges in using these tools effectively.

The following are the charts corresponding to the descriptive analysis for the Results Section for the Eighth Category, AI Tools for Design Generation, Editing, and Automation Tools, as shown in Figure 9.

4.2. Statistical Analysis

4.2.1. Hypothesis 1 (Chi-Square Test for Familiarity, Usage Frequency, and Challenges)

• Null Hypothesis (H₀): There is no significant difference in familiarity, usage frequency, and challenges across the eight tool categories.
• Alternative Hypothesis (H₁): There is a significant difference in familiarity, usage frequency, and challenges across the eight tool categories.

A.

Familiarity Across Categories, as shown in

Table 33. Familiarity with digital tools across all categories (authors).

Category	Very Familiar (%)	Somewhat Familiar (%)	Not Familiar (%)	Significance (p-Value)	Significant?
CAD and BIM Software	80	15	5	-	No
Parametric and Algorithmic Design	60	30	10	3.71 × 10−15	Yes
3D Modeling and Rendering Tools	75	20	5		No
Digital Fabrication and Prototyping	65	25	10		No
Virtual and Augmented Reality Tools	50	40	10		No
Collaborative Design and Communication	70	25	5		No
Image Editing and Post-Production	85	10	5		No
AI Tools for Design Generation	40	30	30		No

, Figure 10.

Statistical analysis highlights how Parametric and Algorithmic Design tools show significant differences in familiarity, with a p-value of 3.71 × 10⁻¹⁵, indicating much lower familiarity compared to other categories like CAD and BIM Software and Image Editing tools.

B.

Usage Frequency Across Categories, as shown in

Table 34. Usage frequency of digital tools across all categories (authors).

Category	Daily (%)	Weekly (%)	Monthly (%)	Rarely (%)	Significance (p-Value)	Significant?
CAD and BIM Software	50	30	15	5	-	No
Parametric and Algorithmic Design	20	40	30	10	1.40 × 10−25	Yes
3D Modeling and Rendering Tools	40	35	20	5		No
Digital Fabrication and Prototyping	30	40	25	5		No
Virtual and Augmented Reality Tools	25	35	30	10		No
Collaborative Design and Communication	50	35	10	5		No
Image Editing and Post-Production	70	25	5	0		No
AI Tools for Design Generation	10	25	40	25		No

, Figure 11.

Statistical analysis highlights how Parametric and Algorithmic Design tools have a significant difference in usage frequency (p-value = 1.40 × 10⁻²⁵), with these tools used much less frequently than tools like CAD and BIM Software and Image Editing.

C.

Challenges Across Categories, as shown in

Table 35. Challenges of digital tools across all categories (authors).

Challenge	CAD and BIM Software (%)	Parametric and Algorithmic Design (%)	3D Modeling and Rendering Tools (%)	Digital Fabrication and Prototyping (%)	Virtual and Augmented Reality (%)	Collaborative Design (%)	Image Editing (%)	AI Tools for Design Generation (%)	Significance (p-Value)	Significant?
High Software Cost	60	60	50	60	65	45	60	60	1.0	No
Lack of Training	55	55	40	50	50	50	55	50	1.0	No
Limited Access to Computers	45	45	30	40	50	40	40	45	1.0	No
Technical Difficulties	30	40	25	35	40	30	25	35	1.0	No

, Figure 12.

Statistical analysis highlights that no significant differences were found in the challenges faced by faculty across tool categories (p-value = 1.0). Challenges such as high software costs, lack of training, and limited access to computers are common across all categories.

4.2.2. Hypothesis 2 (T-Test for Educational Impact)

• Null Hypothesis (H₀): There is no significant difference in the educational impact between tool categories.
• Alternative Hypothesis (H₁): There is a significant difference in educational impact between tool categories.

-

Educational Impact Across Categories, as shown in

Table 36. Educational impact of digital tools across all categories (authors).

Category	Strongly Agree (%)	Agree (%)	Neutral (%)	Disagree (%)	Significance (p-Value)	Significant?
CAD and BIM Software	70	20	10	0	-	No
Parametric and Algorithmic Design	50	35	10	5	0.0034	Yes
3D Modeling and Rendering Tools	60	30	10	0		No
Digital Fabrication and Prototyping	55	35	10	0		No
Virtual and Augmented Reality Tools	45	35	15	5		No
Collaborative Design and Communication	65	30	5	0		No
Image Editing and Post-Production	85	10	5	0		No
AI Tools for Design Generation	20	40	30	10		No

, Figure 13.

Statistical analysis highlights how Parametric and Algorithmic Design tools show a significant difference in educational impact (p-value = 0.0034), with CAD and BIM software and image editing tools having higher educational impact than specialized tools like AI Tools.

4.2.3. Interpreting Results to Hypotheses

The Chi-Square analysis showed a statistically significant difference between Parametric and Algorithmic Design tools and more conventional tools, such as CAD and BIM software (p = 3.71 × 10⁻¹⁵). This finding suggests that Parametric and Algorithmic Design tools are felt to be less familiar among the participants. Furthermore, the same analysis showed a significant decrease in the frequency of usage of these tools with respect to the other categories, with a p-value of 0.84. Although participants see Parametric and Algorithmic Design tools as valuable, the extent to which they can be integrated into daily instructional practices is limited. No significant differences were found between tool categories in terms of challenges reported, with barriers such as high cost of software and a lack of training being reported across all types of tools (p = 1.0).

The data only partially confirm Hypothesis 1. Significant variations in terms of both familiarity and frequency of use are apparent for the Parametric and Algorithmic Design tools compared to other digital instruments. However, the lack of substantial differences in perceived challenges between tool categories indicates that some general challenges, mainly financial constraints and a lack of training, apply to all digital tools. These results highlight the need for focused interventions to increase familiarity with and use of advanced design tools.

The independent samples T-test showed a significant difference in perceived educational impact between Parametric and Algorithmic Design tools and other categories of software (p = 0.0034). Consequently, participants described a higher level of educational influence of Parametric and Algorithmic Design tools compared to more traditional tools such as CAD, BIM, and image-editing software. In contrast, other categories, such as VR/AR and AI-based design tools, did not show substantial differences in perceived educational impact; a lot of these tools were reported to be infrequently used and very specialized.

As far as Hypothesis 2 is concerned, the results are somewhat supported. Parametric and Algorithmic Design tools provide tremendous educational results, fitting their claimed role in developing higher-order cognitive processes as defined in Bloom’s Digital Taxonomy. Nonetheless, the educational impact of specialized tools (e.g., AI and VR/AR) seems to be in its infancy, with little adaptation to the core curricular settings. The results indicate that although these tools are promising, their widespread use and perceived educational benefits are emergent in relation to design education in Jordan.

In summary, the statistical analyses support that familiarity and frequency of use are quite different across categories of digital tools, with Parametric and Algorithmic Design tools having lower degrees of both. Simultaneously, the educational effect of these same tools is far greater than that of more traditional software. However, challenges related to adoption, especially high cost and inadequate training, are consistent across all categories, not differing significantly and thus represent universal barriers that need to be addressed in order to encourage more widespread incorporation of digital tools into design education. The summary is shown in

Table 37. Summary of statistical tests and results for hypotheses (authors).

Hypothesis	Statistical Test	p-Value	Conclusion
Hypothesis 1 (Familiarity): Significant difference in familiarity	Chi-Square	3.71 × 10−15	Reject H0, significant difference
Hypothesis 1 (Usage Frequency): Significant difference in usage frequency	Chi-Square	0.84	Fail to reject H0, no significant difference
Hypothesis 1 (Challenges): Significant difference in challenges	Chi-Square	1.0	Fail to reject H0, no significant difference
Hypothesis 2 (Educational Impact): Significant difference in educational impact	T-Test	0.0034	Reject H0, significant difference

.

4.3. Theoretical Framework Analysis

4.3.1. Application of the SAMR Model

The integration of digital tools into design education follows a systematic progression as outlined by the SAMR model (Substitution, Augmentation, Modification, and Redefinition). Conventional instruments, like AutoCAD and SketchUp, largely occupy the Substitution and Augmentation tiers. At the same time, more advanced technologies that are based on VR/AR and artificial intelligence AI, AI-based design tools have the theoretical ability to reach the Modification and Redefinition phases, though their practical implementation is sparse. SAMR categorizations are summarized in

Table 38. SAMR categorization summary (Authors).

Digital Tool Category	SAMR Stage	Explanation
CAD And BIM Software	Substitution/Augmentation	These tools replace traditional design methods (e.g., hand-drawing) and enhance workflows, particularly with BIM in Revit.
Parametric and Algorithmic Design Software	Augmentation/Modification	These tools introduce parametric design, transforming how designs are created, from simple adjustments to highly dynamic models.
3D Modeling and Rendering Tools	Substitution/Augmentation	These tools replace traditional rendering and visualization techniques, offering more efficient ways to visualize complex designs.
Digital Fabrication Tools	Augmentation	3D printers and CNC machines allow for physical models to be produced more accurately and quickly, adding value to the prototyping process.
VR/AR Tools	Modification/Redefinition	These immersive technologies enable interactive and experiential design exploration, redefining how students interact with their designs.
Collaborative Tools	Augmentation	These tools enhance the collaborative design process, enabling real-time communication and collaboration across different teams and locations.
Image Editing Tools	Substitution/Augmentation	These tools replace manual design processes and offer advanced editing features, facilitating rapid iteration and refinement of visual designs.
AI Tools for Design Generation	Redefinition	These tools redefine how designs are generated, utilizing AI to create innovative and novel solutions, automating tasks traditionally performed manually.

.

The empirical evaluation of digital tools shows that AutoCAD and SketchUp are substitution tools (replacing manual drafting processes with digital processes), and Revit enhances these processes by integrating the building information modeling (BIM) process. More sophisticated platforms, such as Grasshopper and Dynamo, go beyond the basic substitution and allow for dynamic and parametric designs, placing them in the Augmentation and Modification categories of the SAMR model.

4.3.2. Application of Technology Acceptance Model (TAM)

The Technology Acceptance Model was used to explain the relationships in the use of digital tools, perceived ease of use (PEOU), perceived usefulness (PU), and uptake of digital tools. Instruments that provide more usability and have explicit educational advantages are more likely to be adopted by end-users. TAM regression analysis results are shown in

Table 39. Regression analysis results for TAM (authors).

Digital Tool Category	PEOU	PU	Adoption Rate
CAD and BIM Software	High	High	High
Parametric and Algorithmic Design Software	Moderate	High	Moderate
3D Modeling and Rendering Tools	Moderate	High	High
Digital Fabrication Tools	Moderate	High	Moderate
VR/AR Tools	Low	High	Low
Collaborative Tools	High	High	High
Image Editing Tools	High	High	High
AI Tools	Low	High	Low

.

Results obtained from the TAM framework show that tools such as AutoCAD and SketchUp have both high ease of use and appreciable usefulness, hence yielding high adoption rates by students and faculty. On the other hand, VR/AR and AI-based tools face adoption barriers that can be attributed to their low perceived ease of use, which is a constraint in their widespread implementation, despite any perceived usefulness.

4.3.3. Application of Bloom’s Digital Taxonomy

Based on Bloom’s Digital Taxonomy, the tools were assessed on the cognitive engagement work they enable, from the lower end of thinking skills (application and understanding) to the higher end (analysis and creation). Results are shown in

Table 40. Cognitive engagement levels by tool (authors).

Digital Tool Category	Bloom’s Level	Cognitive Engagement
CAD and BIM Software	Apply/Understand	Supports practical application of design principles and understanding of technical concepts.
Parametric and Algorithmic Design Software	Analyze/Create	Encourages analysis of design relationships and creation of complex parametric models.
3D Modeling and Rendering Tools	Apply/Create	Facilitates applying visualization techniques and creating realistic design representations.
Digital Fabrication Tools	Apply	Helps students apply design concepts to physical models, reinforcing spatial understanding.
VR/AR Tools	Analyze/Create	Engages students in analyzing and creating immersive design environments for interactive learning.
Collaborative Tools	Apply/Analyze	Promotes collaboration and problem-solving, enhancing the design analysis process.
Image Editing Tools	Apply/Create	Enables students to apply creative techniques and produce refined visual outputs.
AI Tools	Create	Fosters creative design generation through AI-based tools, allowing new approaches to design creation.

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The application of Bloom’s Taxonomy shows that using tools such as AutoCAD and Revit mainly uses the lower order of the thinking cycle (applying and understanding), while using tools such as Grasshopper and VR/AR use the higher-order thinking skills of analysis and creation. This highlights the potential of innovative digital tools to promote critical thinking and creative problem-solving.

5. Discussion

This study explored the integration of digital tools in design education in Jordanian universities, focusing on how educators use these tools, their familiarity with them, and the perceived educational impact. The findings indicate a strong reliance on traditional tools like AutoCAD and SketchUp, along with emerging technologies such as parametric design software and virtual reality (VR), albeit with varying levels of familiarity, usage frequency, and perceived impact.

5.1. Familiarity with Digital Tools

The results showed that some tools are already well-known in design education. For example, AutoCAD (80%) and SketchUp (75%) are very familiar to teachers, which is in line with trends around the world. Because they have been around for so long in the industry, these tools are essential for both architectural and interior design education. This finding corroborates previous studies, which underscores the essential function of CAD tools in equipping students for practical design applications [25]. But Rhino and Archicad had lower familiarity rates, which means that their use in design education in Jordan is still growing. These tools are becoming more popular, but many people think they are too specialized and need more training to use them well.

This is in line with notions focusing on how specialized tools, like Rhino and Archicad, are not often included in the curricula [24,26]. Grasshopper for Rhino (70%) and Dynamo for Revit (50%) were two more advanced tools that people were somewhat familiar with. This is in line with studies highlighting that Parametric and Algorithmic Design tools are becoming more common in design education but still have problems with teacher expertise and fitting into the curriculum [26,29,30,31]. Houdini and TouchDesigner exhibited even lesser familiarity, indicating that these tools remain marginal within mainstream design education, a finding corroborated by previous studies on interactive design environments [46,48].

5.2. Usage Frequency and Educational Impact

SketchUp and AutoCAD were the most popular tools, with 60% of respondents saying they used SketchUp every day. This frequency shows that it has a user-friendly interface and can be used for a wide range of design tasks. This supports previous ideas that stress the importance of easy-to-use tools in encouraging daily design practices [45]. On the other hand, only 10% of respondents used Rhino every day, which shows how hard it is to use these more advanced tools in everyday teaching.

The educational effects of these tools were different as well. SketchUp had the most positive effect, with 80% of people strongly agreeing that it helps with teaching. Aligning with studies focusing on how the tools that are easy to use, like SketchUp, are important for encouraging creativity and getting students involved [19,34]. People had mixed feelings about tools like AutoCAD and Revit, but they were still seen as good. The problems with Revit, especially its complicated interface, are that while BIM tools have great potential, they need a lot of training and adjustment to work well in schools [8,17,18].

The parametric and algorithmic tools (like Grasshopper and Dynamo) were used less often, but they did have a moderate effect on learning. This indicates that although educators recognize their potential, these tools are predominantly utilized in advanced or specialized courses. The results support notions that 3D printers and CNC milling machines are good for making prototypes, but their high cost and the fact that they are hard to learn make them hard to use by a lot of people [24].

5.3. Challenges in Digital Tool Integration

The high cost of software was the biggest problem in all categories, especially for programs like Revit, Rhino, and Houdini. This finding is also discussed in relevant studies of how digital tools can be expensive for schools, especially in developing countries. Also, a lack of training and limited access to computers were common problems that made integration less effective, especially in making use of the collaboration platforms [22,23,36]. Studies also stress the need for professional development and better infrastructure, saying that these problems must be solved in order to get the most out of digital tools in design education [17,47,48].

The study interestingly found that AI tools for design generation, like Runway ML and DALL·E, were less well-known and used, but they showed promise for having an impact on education. This means that AI tools are still new to design education, but they could be very useful for creative purposes. Previous studies also highlight how digital storytelling and AI can improve design communication and creative thinking [39,40].

5.4. Implications for Design Education in Jordan

The results of this study give us useful information about how well digital tools are being used in design education in Jordan. Even though people know how important digital tools are, they are hard to use together because of things like cost, lack of resources, and the need for more training. These challenges are similar to those found in studies of design education in other parts of the world. Previous studies stress the need to get around technical and infrastructure problems so that digital tools can be used effectively [38].

According to the study, new technologies, especially artificial intelligence (AI) and virtual reality (VR), are promising, but they demand more serious attention to the curriculum design and training of educators. With the introduction of new technologies, their implementation has to be pedagogically sound, and teachers have to undergo constant professional development in order to make sure they are used correctly [32,33,35].

On the whole, the introduction of digital tools into the design education in Jordan is on the way to positive changes; however, the problems of costliness, the lack of access to resources, and the lack of training are still present. Challenges such as more institutional backing, cooperation with the technological industry, and increased teacher education are critical to unlock the full potential of digital tools in teaching, learning, and creativity in design education.

5.5. Critical Interpretation (Frameworks)

The results of this study give indisputable proof that, although digital tools have been introduced in the field of design education in Jordan, their utilization has mostly remained confined to the Substitution and Augmentation stages of the SAMR model. These levels indicate that the tools are mostly used to replace the old methods (e.g., AutoCAD as a replacement for hand-drawn designs) or for adding to the current ones (e.g., Revit as an addition of building information modeling functionalities to architectural designs). This observation is consistent with the current trend of what we see in other countries, where digital tools are still considered as adjuncts of traditional pedagogical methods rather than as transformative agents.

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SAMR Model: Limited Transformation of Learning Processes

The SAMR model has been a useful tool to understand the integration of technology in design education. It shows that, although the use of tools like AutoCAD and SketchUp is widespread, for the most part, they still exist in the Substitution stage. These tools have a great deal to offer in terms of efficiency and precision, but they do not fundamentally change the way that students conceptualize design. Conversely, those instruments like Grasshopper and Revit, moving into the stage of Augmentation, increase the ability of students to interact with design data in more sophisticated ways without, at this point, redefining the overall learning experience. Technology like VR/AR and AI based applications that have the potential to reach the Modification and Redefinition stages have the potential to provide immersive and generative design experiences, but the adoption of these technological solutions is limited.

The outcomes support the idea that, while these tools have the potential to revolutionize design education, based on theoretical frameworks such as SAMR, it is clear that the comprehensive transformation has not yet occurred. The use of tools is often embedded in traditional, non-transformative ways. VR/AR and AI tools, although very promising in their transformation potential, are still in the early stages of adoption. Barriers to widespread utilization—including complexity and hardware requirements—agree with the results of previous studies of technology adoption in education, where the process of change is often slow and uneven.

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TAM: Ease of Use and Usefulness are Key Barriers

According to the Technology Acceptance Model (TAM), the perceived ease of use (PEOU) and perceived usefulness (PU) of tools are the major factors affecting the adoption of tools. The present findings lend support to this, with AutoCAD, SketchUp, and image editing tools having high adoption rates due to their perceived ease of use and utility in the design process. In contrast, more complex devices such as VR/AR, as well as AI-based design platforms, face challenges with respect to low perceived ease of use, which is due to their technical complexity and high learning curve, notwithstanding their high potential usefulness towards fostering creativity and interactivity.

The challenges of adopting VR/AR and AI tools can be explained to a large extent using the TAM framework, which argues that a tool is not only required to be useful but also easy to use. The complexity and barriers to learning inherent in these advanced technologies prevent their uptake, as is reflected in their low adoption rates. This is a great dismay to design education, as such tools are considered to be very useful but underutilized due to perceived difficulties in mastery and lack of immediate usefulness.

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Bloom’s Digital Taxonomy: Engaging Away and Adopting Tools

The use of Bloom’s Digital Taxonomy further emphasizes the possibilities for the use of VR/AR and AI tools to engage students in higher-order thinking. Instruments like Grasshopper and Revit support the application and understanding of design concepts, whereas advanced tools lead students to generate and analyze a new design solution. The data shows that AI platforms like Runway ML and DALL-E make it easier for students to create new ideas for designs with AI that allow them to think about things they have never considered before. Similarly to this, VR/AR tools have the ability to create immersive experiences, where students are able to analyze and manufacture in virtual spaces, signaling a shift in cognitive engagement to higher-order thinking.

Nonetheless, adoption of these tools still faces limitations due to the difficulty in finding effective ways of using them and the unfamiliarity with their immediate benefits. TAM results indicate ease of use and perceived usefulness as critical to adoption. Bloom’s Digital Taxonomy, however, suggests that even high cognitive engagement potential cannot overcome usability and perceived utility barriers prior to comprehensive integration into design education as a realization.

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Implications of Design Education

The findings imply the suggestion that, despite there being a high potential for VR/AR and AI to revolutionize design education, integration of these tools is currently hindered by both technical and pedagogical challenges. From an SAMR perspective, the transformation of design education is still in its early stages, and the majority of tools are still in the Substitution and Augmentation categories. This means that even though digital tools make the learning experience better, they have not yet radically changed the engagement of students with design.

From a TAM standpoint, it is important to reduce barriers such as ease of use and perceived usefulness for the successful adoption of more advanced tools. Educators and institutions, therefore, need to provide specific training, ongoing support, and the necessary infrastructure to increase accessibility and utility to learners.

Finally, within the context of Bloom’s Digital Taxonomy, it is apparent that VR/AR and AI tools have the potential to engage students at elevated cognitive levels. However, widespread utilization will require overcoming current barriers to adoption, including technical complexity and the fact that, for students, there is no immediate utility. Such tools could help enable more in-depth involvement and creative exploration, and thus, higher-order thinking skills such as analyzing, creating, and evaluating, which are important for the future of design education.

6. Conclusions

This paper has explored the use of digital tools in architectural and interior design education at universities in Jordan and has explored the familiarity of educators with these tools, patterns of their use, perceived educational effects, and the barriers faced to their use. The findings reveal that the adoption of digital tools in design education follows a predictable pattern, aligned with HCI principles of usability and user experience. The results show that tools like AutoCAD and SketchUp are more widely adopted because they are perceived as easier to use and fit into the established pedagogical framework of active learning and constructionist approaches, as opposed to other tools like Rhino, Revit, and parametric design software that are not as often used in the daily teaching practice. Additionally, new platforms are not utilized in the classroom, such as AI-based design systems and VR. Newer tools, like AI and VR, face barriers of complexity and perceived lack of immediate usefulness, which aligns with TAM’s emphasis on perceived ease of use and perceived usefulness. These tools, while offering transformational potential, are still at the Substitution and Augmentation stages of the SAMR model, and future development in digital pedagogy will require educators to adopt a more constructivist approach to teaching these advanced technologies. Software cost is still a significant barrier to the wider use of tools, and additional training and access to high-performance computing are factors that hinder these tools. Even with such challenges, most respondents consider digital tools in a positive light with regard to learning outcomes, particularly SketchUp, AutoCAD, and 3D modeling and rendering programs like Lumion and V-, ray, which are seen to support student learning and creativity.

6.1. Key Findings

• Knowledge and Application: AutoCAD and SketchUp represent the most accepted and common types of traditional tools, and Rhino and parametric design software are less well-known and utilized.
• Educational Effect: SketchUp and AutoCAD are generally thought to be the most effective when used in education, especially to provide interest and creativity to students, but Rhino and advanced AI tools produce more mixed outcomes.
• Challenges: The biggest problems that make it hard to use digital tools well in design education are issues such as high software costs, a lack of training, and not being able to obtain the right hardware.

6.2. Limitations

Despite the contributions the study offers, it does have several methodological limitations. First, the relatively small sample size of Jordanian universities limits the generalizability of the findings to broader, more diverse educational settings. Additionally, the regional focus on Jordan may not fully reflect the digital transformation trends in other countries or educational systems. Therefore, future studies should aim to include a larger and more diverse sample, potentially extending beyond the regional context to compare digital tool integration across different cultures and institutional structures.

6.3. Recommendations

• Curriculum Integration: The curriculum would have to include higher technology tools like Rhino, Revit, and AI-design platforms, which should be presented to the students of architecture and interior design in such a way that they could understand and use them.
• Professional Development: Universities ought to fund training to enhance the level of expertise of the educators on emerging digital tools, which they might have to do through an agreement with the industry stakeholders to bridge the knowledge gap.
• Resource Allocation: Organizations might overcome the cost constraints by obtaining software licenses, the search for open-source options, or industry partnerships.
• Technical Infrastructure: Schools have to invest in the modernization of computer labs and provide students with powerful computers that can run sophisticated software, especially those applications in digital creation and VR/AR.
• Exploiting Emerging Tools: The study proposes strategies to improve design education in the future [49]. The paper suggests that future development of design education must involve an increased focus on the integration of technologies, including augmented reality (AR), virtual reality (VR), and AI design generators. Research and faculty development in these areas will be critical in the development of interesting and thought-provoking learning environments.

6.4. Theoretical Implications for Future Research

The utilization of the SAMR framework, Bloom’s Digital Taxonomy, and the Technology Acceptance Model provides a powerful framework within the frames of which the phases of the digital tool adoption in the design education might be viewed. These theoretical models are largely consistent with the empirical findings, which show that despite the fact that Jordanian design education is becoming more integrated with the use of digital tools, it is still mostly in the Substitution and Augmentation levels, with little progression to the Modification and Redefining levels, which the SAMR model postulates. The contribution adds to the expanding body of knowledge on the digital transformation of higher education; however, more studies are needed to study the influence of emerging technologies and their roles in advancing design education to new levels of transformation. Future research needs to consider the efficacy of virtual reality, augmented reality, and artificial intelligence technology to develop and nurture higher-order cognitive abilities as well, as per the taxonomy created by Bloom.