Artificial Intelligence Role in Advancing the Design Education Process

doi:10.21203/rs.3.rs-5102946/v1

The current research paper explores the creative integration between science and arts, highlighting its significance in intellectual and professional fields such as architectural design, advertising design, and fashion design. This integration aims to enhance creativity and innovation among learners by providing them with tools and methodologies that develop their creative abilities, including intellectual fluency, and examining the impact of this integration on design outcomes.

It also highlights the importance of updating and developing educational methods, with a focus on the role of artificial intelligence as an assistive tool in this context. The study emphasizes the necessity of human oversight to ensure a balanced and creative learning environment, as artificial intelligence is currently incapable of fully performing human tasks.

In the applied section of the research, two different educational experiments were conducted in 2024 at the Architecture College, Yarmouk Private University, on a single group consisting of 15 students, selected as a purposive target sample. In the first experiment, the traditional method was employed for teaching and supervising a swimming pool hall design project, while in the second experiment, an approach based on artificial intelligence was utilized for a museum design project.

Finally, the study concluded that the integration of science with the arts in design education using artificial intelligence can enhance learners' problem-solving skills, innovation, and critical thinking. It also enables them to develop new skills that contribute to a comprehensive understanding of design processes.

The achievement of the aforementioned objectives was facilitated by a series of procedures using the descriptive methodology, which involves the application of certain artificial intelligence tools in the design education process, followed by their interpretation and discussion. Subsequently, the applied analytical methodology was employed in the research's practical section, where the results of the studied group were examined and analyzed using SPSS (a software specialized in statistical data analysis).

Architecture, Design and Planning

Creative Thinking

Higher-Order Thinking

Design Problem Solving

Adaptive Design Thinking

Wherever the following terms appear in the text, they refer to the following:

Design: Refers to design in its full creative scope, encompassing various disciplines such as architectural design, interior design, advertising design, fashion design, and others. It is a field that integrates both scientific and artistic aspects.

Intellectual Fluency in Design: Refers to one of the abilities associated with creative thinking, characterized by the capacity to generate the maximum number of design ideas within a limited timeframe. This ability facilitates the emergence of ideas that are unique and original.

Critical Thinking: Refers to the systematic evaluation of ideas and solutions, aiding learners in analyzing and assessing the quality of ideas produced through intellectual fluency. This ensures that the final designs are not only innovative but also practical and suitable for use (Paul & Elder, 2006).

Artificial Intelligence: A branch of computer science and mathematics concerned with the design and development of systems and programs that can simulate and emulate human intelligence. These programs are capable of thinking, decision-making, learning, problem-solving, and autonomously performing tasks at a level comparable to human abilities. This is achieved through the use of computing, data, machine learning, natural language processing, and other related technologies.

Higher-Order Thinking: requires the integration of creative thinking (right hemisphere of the brain) and critical thinking (left hemisphere of the brain). It is characterized by the attributes of creative thinking, including fluency, originality, and flexibility
(Runco, 2014).

Creative Thinking + Critical Thinking = Higher-Order Thinking.

Adaptive Design Thinking: A cumulative, constructive process that adopts the philosophy of continuous education and training. It aims to support thinking in order to develop solutions for constant problems and crises, both during and after the university education phase. This type of thinking addresses design problems characterized by their changing nature. Consequently, it is a type of thinking that can be cultivated and developed as it is considered a skill amenable to growth.

Research Problem: Learners face a fundamental challenge when generating initial design ideas, which is the need for innovation and intellectual creativity through the generation of the maximum number of alternatives and logical solutions in the shortest possible time.

Research Question:

Can artificial intelligence assist learners in achieving a design outcome that is distinct from those produced through traditional methods?

Research Objectives

This research aims to study the effectiveness of artificial intelligence in saving effort and time for learners by offering a range of effective solutions to design problems. Additionally, the study seeks to evaluate the extent to which the design outcomes produced using artificial intelligence are distinguished from those generated through traditional design methods.

Significance of the Study

The research contributes to enhancing the cognitive level of learners and developing problem-solving skills in the design process through the use of artificial intelligence techniques. These techniques assist in improving learners' mental capacities and achieving more original and effective design in a shorter time.

Research Methodology

The current study employs two main methodologies to examine the significance of using artificial intelligence in design education:

1-Descriptive Methodology: Identifying artificial intelligence techniques that can assist in the design education process.

2-Applied Analytical Methodology: Analyzing the extent to which learners benefit from artificial intelligence techniques in comparison to traditional design education methods.

The present study is part of a body of research focused on enhancing the educational process by leveraging open education systems to teach and develop adaptive design thinking. It also emphasizes the mechanism of building cognitive abilities supported by this system. These steps are integral to the advancement of design education across various creative disciplines. Design is considered an artistic science that relies on both innate talent and acquired creativity. (Idelbi, 2018)

Today, there is a need for innovative approaches and educational techniques that help develop design thinking to produce original creative outputs that meet professional needs, address societal issues, and consider environmental challenges. Among these techniques, artificial intelligence (AI) stands out prominently.

To achieve this, educational institutions should support university teaching methods with modern and rapidly evolving learning technologies. This enables learners to master the essential skills necessary for continuous self-directed learning. Such an approach contributes to the development of adaptive design thinking, which enhances design skills regardless of the methods and technologies that may appear in the future. (Floridi,2014)

Learning to solve problems and make decisions based on evolving variables can be considered one of the primary goals of adaptive design thinking. This type of thinking is characterized by dynamism, innovation, and continuity, relying on higher-order thinking (creative thinking + critical thinking). (Idelbi,2018)

Mastering certain self-directed learning skills may provide a pathway to teaching and developing adaptive thinking.

One of the most important self-directed learning skills is critical thinking, which involves the ability to analyze, critique, and evaluate. This skill is essential for selecting among the various ideas and solutions that can be implemented. (Idelbi, 2016)

The relationship between intellectual fluency and critical thinking lies in the fact that intellectual fluency enables the generation of numerous ideas and fosters innovation by encouraging free and unconstrained thinking. This leads to more creative and diverse designs. On the other hand, critical thinking evaluates and refines these ideas to arrive at the best possible solutions. Without intellectual fluency, a designer might be limited in the number of ideas they can explore, and without critical thinking, the innovative ideas may prove impractical or unsuitable. (Schleicher,2024)

Artificial intelligence can be a powerful tool in creative education. Its use in data analysis and providing instant feedback to learners helps in improving their ideas and designs. Additionally, AI can be employed to quickly and efficiently create and test prototypes of ideas (Li et al., 2019).

Teaching Design Using Artificial Intelligence

Integrating Artificial Intelligence (AI) into design education can revolutionize the way design is taught. AI can assist in generating a wide range of innovative design concepts, reducing the time and effort required to develop ideas. Additionally, it can aid in developing critical thinking and problem-solving skills.

1. The Design Process:

The design process is defined as a series of steps or stages that a designer follows to develop an innovative and appropriate design solution that meets users' needs or addresses the presented problem. This process begins with identifying and understanding the problem, followed by gathering and analyzing information. It then involves generating ideas and producing design solutions, developing and testing prototypes, and finally, implementing the final design with ongoing evaluation to refine the proposed solutions. (Lidwell, 2003)

According to the study by (Idelbi,2018), the traditional design process typically involves several key stages, including:

1-Understanding the Design Problem

This involves analyzing the problem and understanding its requirements.

2-Information Gathering

This includes collecting data and information relevant to the design problem.

3-Concept Generation

This stage involves generating design concepts.

4-Development

This includes developing and refining the design concepts.

AI-Enhanced Design Process

According to the study by (Li, D., et al,2019), the AI-enhanced design process involves utilizing tools and techniques supported by artificial intelligence at each stage of the traditional design process. For example:

1-Understanding the Design Problem

AI tools and techniques can be used to analyze the problem, providing insights and recommendations.

2-Information Gathering

AI tools and techniques can be employed to collect and analyze large volumes of data, providing diverse and potentially innovative insights and patterns.

3-Concept Generation

Design concepts can be generated using techniques such as Generative Adversarial Networks (GANs) and neural style transfer. ⁽⁾

4-Development

AI can be used to develop and refine design concepts through techniques such as optimization and simulation.

Thus, relying on artificial intelligence can be a transformative shift in design education by introducing learners to the following:

AI-Driven Design Tools and Their Utilization: For example, AI can assist in generating multiple design options based on a set of information, allowing learners to explore various possibilities and design solutions. Analyzing design outputs and providing feedback on usability, sustainability, or aesthetics is crucial for developing learners' critical thinking skills. (Pacheco-Mendoza,2023)

Application Name	Design Applications	Official Website
Copilot	Architectural Design – Interior Design – Advertising Design – Fashion Design	www.copilot.ai
ChatGPT	Discussion of Design Ideas	www.chat.openai.com
Groq (Lama 3)	Discussion of Design Concepts	www.groq.ai
Midjourney	Architectural Design – Interior Design – Advertising Design – Fashion Design	www.midjourney.com
Look X	Furniture Design – Interior Design – Architectural Design	www.lookx.ai
Fotor	Advertising Design – Fashion Design	www.fotor.com

Case Studies and Real-World Applications: Studying real-world examples in design, or exploring design using VR technology in various fields such as industrial design, architectural design, graphic design, and fashion design, is highly beneficial. This helps learners understand the practical applications of artificial intelligence and its role in providing alternative design solutions, as well as how to use AI to address design challenges that arise from those solutions. (Idelbi, 2024)

Integrating artificial intelligence into design education represents a revolution in how we teach and learn design. Leveraging AI's capabilities provides a more engaging, efficient, and effective learning experience for students. However, teaching design with AI presents both opportunities and challenges. While AI can enhance creativity and efficiency, it is essential to recognize its limitations and ensure that human creativity and innovation are not lost in the process. Therefore, educational institutions must be adaptable to technological changes and develop educational methods and programs that ensure the integration of science with the arts, complementing human creativity and innovation with AI technologies. (Li et al., 2019)

3- Benefits of AI in Design Education

According to the study by (Floridi, 2014), integrating artificial intelligence into design education can bring numerous benefits, including:

1-Enhancing Creativity

AI can assist in generating innovative design concepts, allowing students to focus on developing critical thinking and problem-solving skills.

2-Improving Efficiency

AI can automate repetitive and time-consuming tasks, enabling learners to concentrate on higher-level design decision-making.

3-Data-Driven Design

AI can provide valuable insights and patterns from large datasets, allowing students to make more informed design decisions.

4-Personalized and Guided Learning

AI can be used to support individualized learning experiences by providing tailored feedback and guidance for each student.

5-Collaboration

AI can facilitate collaboration among students from different disciplines, enabling them to work together more effectively.

Although artificial intelligence (AI) offers numerous benefits in design education, it also faces certain limitations and challenges. AI cannot entirely replace human expertise and creativity, and a complete reliance on AI may result in an environment lacking the human touch and innovation (Floridi, 2014).

Therefore, it is essential to develop and update educational methods and programs to enhance students' cognitive and creative abilities, integrating artificial intelligence in a way that complements human creativity and innovation (Fullan, 2013).

Hence, educational institutions should play a vital role in achieving this integration between science and art. They must be capable of adapting to rapid technological changes and developing new teaching methods and programs that meet the needs and aspirations of students (Schleicher, 2018). Developing educational programs requires close collaboration among teachers, students, and administrators to ensure the best possible outcomes.

Applied Study

An experiment was conducted with a group of architectural design students at the University of Yarmouk in the countryside of Damascus. This group was considered a purposive sample ⁽⁾ of students enrolled in the architectural design course AD5. The group consisted of 15 male and female students.

The experiment was conducted during class sessions by comparing the results of two project grades in the second semester of 2024, under the supervision of the same instructor. Subsequently, the results were compared with those of a reference group to evaluate the impact of the intervention involving AI-enhanced design processes.

The first project involved designing a sports hall and an indoor swimming pool, while the second project was a museum design. The first project was assigned and monitored using traditional teaching methods, whereas the second project was handled using an alternative approach (incorporating AI in explanations and monitoring). In this approach, students were presented with problem-solving methods through the generation of a range of images and projects created using various AI applications. The aim was to train them on the concept of cognitive fluency, followed by training in critique, evaluation, and analysis. This enhanced their ability to express themselves and understand adaptive design thinking. Additionally, students learned the importance of analysis, critique, and evaluation (critical thinking) of any proposed design outcomes generated by AI applications. Not all results produced by AI meet the desired requirements in terms of execution style, cost, aesthetics, and functionality.

The experiment was conducted with a pre-intervention semester project (before using the AI approach—designing a sports hall and an indoor swimming pool) and a post-intervention semester project (two months later—using the AI approach for explaining and monitoring the museum design project). The results were as follows:

Student Number	Name	Pre-intervention project grade / 20 points (X)	Post-intervention project grade / 20 points (Y)	Difference between the two grades (d)	Student Number	Name	Pre-intervention project grade / 20 points (X)	Post-intervention project grade / 20 points (Y)	Difference Between the two grades (d)
1	Rawda	15	18.5	3.5	9	Oday	10.3	16.4	6.1
2	Aryam	15	17	2	10	Sidra	18	19	1
3	Tuqa	17	18.5	1.5	11	Julie	18	18	0
4	Muhammad Khalil	16	16	0	12	Dalal	13	18	5
5	Ata’Aallah	19	18	-1	13	Yasser	16	16	0
6	Ahmed	16	19	3	14	Juri	15	17	2
7	Noor	10.5	16.5	6	15	Muhammed Qahiriya	12	12	0
8	Abdul-Alrhman	17	19	2

Experimental Study Methodology

Participants

15 students.

Location and Time

Students of the AD5 design course at the Faculty of Architectural Engineering, University of Yarmouk, in 2024.

Tools

For the pre-intervention project, the final submission was restricted to hand-drawn presentations within the college's studios to ensure equal opportunity. In contrast, for the post-intervention project, students were allowed to submit their work in any format they deemed suitable to express and realize their design ideas. ⁽⁾

Time

2024

Location

Faculty of Architectural Engineering, University of Yarmouk

Projects:

Pre-intervention Project

Design of a sports hall and an indoor swimming pool using traditional methods (before the use of artificial intelligence).

Post-intervention Project

Design of a museum using artificial intelligence to provide alternative solutions and ideas (after the use of artificial intelligence).

Statistical Analysis

To calculate the change in performance between the pre-intervention and post-intervention projects, we use arithmetic average and standard deviation.

Arithmetic average of the Pre-intervention Project (X̄):

Arithmetic average = sum of X ÷ number of students

(15 + 15 + 17 + 16 + 19 + 16 + 10.5 + 17 + 10.3 + 18 + 18 + 13 + 16 + 15 + 12) ÷ 15 students = 15.35 dgrees

Arithmetic average of the Post-intervention Project (Ȳ):

Arithmetic average = sum of Y ÷ number of students

(18.5 + 17 + 18.5 + 16 + 18 + 19 + 16.5 + 19 + 16.4 + 19 + 18 + 18 + 16 + 17 + 12) ÷ 15 students = 2.12 degrees

Arithmetic average of the Differences (d̄):

Arithmetic average = sum of Differences (d) ÷ Number of students

(3.5 + 2 + 1.5 + 0 − 1 + 3 + 6 + 2 + 6.1 + 1 + 0 + 5 + 0 + 2 + 0) ÷ 15 students = 2.12 degrees

Standard Deviation (sd) =

Where (n) is the sample size = 15 students.

Calculating the standard deviation requires computing the differences from the arithmetic average for each value, then squaring these differences, summing them, dividing the total by (n − 1), and finally taking the square root of the result.

Student number	(d − d̄)²	(d − d̄)	Differences d	Students number	(d − d̄)²	(d − d̄)	Differences d
1	2.036	1.427	3.5	9	16.218	4.027	6.1
2	0.005	-0.073	2	10	1.152	-1.073	1
3	0.328	-0.573	1.5	11	4.297	-2.073	0
4	4.297	-2.073	0	12	8.569	2.927	5
5	9.444	-3.073	-1	13	4.297	-2.073	0
6	0.860	0.927	3	14	0.005	-0.073	2
7	15.425	3.927	6	15	4.297	-2.073	0
8	0.005	-0.073	2

From the table: The sum of (d − d̄)² for the students = 71.236

$$\:\sqrt{71.236÷(15-1)}=2.255$$

Reliability Measurement and t-test:

This is a statistical method used to compare two means to determine if there is a statistically significant difference between them. In this case, we will use the t-test to compare students' scores before and after using artificial intelligence in education.

After calculating the t-value, the result can be compared with the tabulated t-values at the appropriate degrees (df = (n – 1) = 14) and the desired level of significance (typically 0.05).

If the calculated t-value is greater than tabulated value, the difference between the pre-test and post-test result is statistically significant.

t = d\ ÷ (sd ÷ √n) = 2.073 ÷ (2.255 ÷ √15) = 3.56

At a significance level of 0.05, the tabulated t-value (from the tables) for (df = 14) is approximately 2.145. Since the calculated t-value is greater than the tabulated t-value, the differences between the pre-project and post-project scores are statistically significant.

Calculation of Pearson's Correlation Coefficient: This refers to measuring the strength of the relationship and the degree of association between two variables.

t-test:

The calculated t-value (3.56) is greater than the tabulated value (2.145) at a 0.05 significance level, indicating that the difference between the pre-test and post-test scores is statistically significant.

Pearson's Correlation Coefficient:

The correlation coefficient (0.888) is close to (+ 1), indicating a high level of consistency between the pre-test and post-test scores. This suggests that the learners' academic performance remained stable after the implementation of artificial intelligence in design education, which may indicate the effectiveness of the educational program at this stage.

Discussion of the Results

Improvement in Performance

an improvement in post-project performance compared to the pre-project was observed in most cases, indicating the effectiveness of using artificial intelligence in enhancing learners' design skills.

Exceptions

some learners did not show significant improvement (e.g., Student 4 and Student 15), which warrants consideration of possible reasons such as individual differences in learning styles.

Effectiveness of the Traditional Approach

the traditional approach may be effective in the initial stages of education, but it might not be sufficient to stimulate critical thinking and intellectual fluency.

3)Effectiveness of Using Artificial Intelligence

the use of artificial intelligence contributes to enhancing intellectual fluency by offering multiple alternatives and encouraging critique and evaluation.

The significant improvement in learners' results after using artificial intelligence confirms its role in developing critical thinking skills. Based on the findings, it can be concluded that the use of artificial intelligence in design education substantially contributes to enhancing students' academic performance by fostering intellectual fluency and critical thinking. Therefore, it is recommended to integrate artificial intelligence into educational curricula to comprehensively and adaptively develop learners' skills.

Afterward, the results of the practical experiment were compared with another reference sample that was randomly selected from among the student groups distributed by the university administration.

Student number	Pre-intervention project grade / 20 points (X)	Post-intervention project grade / 20 points (Y)	Difference between the two grades (D)	Student number	Pre-intervention project grade / 20 points (X)	Post-intervention project grade / 20 points (Y)	Difference between the two grades (D)
S1	15	18	3	S9	12	14	2
S2	15	17	2	S10	13	13	0
S3	10	13	3	S11	14	18	4
S4	16	14	-2	S12	15	12	-3
S5	16	12	-4	S13	15	13	-2
S6	15	18	3	S14	14	18	4
S7	17	14	-3	S15	17	16	-1
S8	15	12	-3

Having an additional reference sample can be beneficial for comparing the impact of the intervention (such as the application of artificial intelligence in design education) on two groups of students: the group that underwent the intervention and the reference group that did not. Analyzing the reference group helps ensure that the observed improvement in scores is attributable to the intervention and not to other unexamined factors. This enhances the accuracy and strength of the conclusions drawn from the study.

Additionally, having a reference sample provides an objective comparison, which strengthens the conclusions and reduces the potential impact of biases. Without a reference sample, it might be challenging to distinguish between the actual effect of the intervention and changes that could naturally occur over time or due to external factors.

Calculation of the Average Scores for the Pre-Project and Post-Project in Both Groups:

Intervention Group: As previously calculated.
Reference Group: The average scores for the pre-project and post-project are calculated using the sum of the provided scores.

Comparison of the Change in Averages Between the Two Groups:

Calculate the change in the average for each group and compare the differences between them to determine the impact of the intervention on the target group compared to the reference group.

Correlation Analysis for Both Groups:

Calculation of Pearson's Correlation Coefficient: Calculate Pearson's correlation coefficient for each group separately (as it was previously calculated for the target group).
Comparison of Correlation Coefficients: Compare the correlation coefficients between the two groups to determine if there is a difference in the stability or consistency of academic performance between them.

1-Calculating the Averages for Each Group:

Reference Group:

Number of students in the reference sample = 15 (the same number as in the first sample, assuming an equal distribution)

Total scores in the pre-test project = 2019, while the scores for the studied group = 227.8

Total scores in the post-test project = 2022, while the scores for the studied group = 258.9

Averages for the reference sample scores:

Average Score for the Pre-Test Project in the Reference Sample:

2019 ÷ 15 = 134.6

Average Score for the Post-Test Project in the Reference Sample:

2022 ÷ 15 = 134.8

2-Calculating the Difference in Averages Between the Two Groups:

Average pre-test score for the intervention group = 16.253

Average post-test score for the intervention group = 17.460

Difference in averages for the intervention group = 17.460 - 16.253 = 1.207

Difference in averages for the reference group = 134.8 - 134.6 = 0.2

The t-value was calculated to compare the differences in averages between the intervention group and the reference sample. The computed t-value = 1.223. When compared with the critical t-value, it was found that the critical t-value = 2.048.

3-Analyzing the Correlation Coefficient for Both Groups:

By calculating the Pearson correlation coefficient between the pre-test and post-test scores for the reference sample, the computed value was (r ≈ 0.093). This correlation coefficient (0.093) indicates a very weak correlation between the pre-test and post-test scores for the reference sample. This implies that the academic performance of the students in the reference sample is not highly consistent between the pre-test and post-test.

While the correlation coefficient for the intervention group was high (0.888), the correlation coefficient for the reference sample was very low (0.093). This indicates that the intervention (such as the application of artificial intelligence in teaching) may have a significant impact on improving the consistency of academic performance compared to the reference group, which did not undergo the intervention.

Final Results:

1-Improvement in Academic Performance: The study demonstrated that the use of artificial intelligence in developing learners' skills has a statistically significant positive effect on their academic performance. It was found that students' academic performance was stable and consistent, indicating the stability of the educational process when integrating artificial intelligence.

2-Enhancement of Cognitive Fluency: The statistics in the current study revealed that artificial intelligence represents a powerful tool for enhancing cognitive fluency and creativity in design education. However, the human element remains crucial for achieving this integration.

3-Artificial Intelligence as an Auxiliary Tool: Artificial intelligence should be viewed as an auxiliary tool for enhancing human thought, rather than a replacement for it. Full reliance on artificial intelligence could lead to an educational environment lacking human sensitivity and innate creativity.

4-Integration of Artificial Intelligence with Creative Thinking: Architects and designers should consider artificial intelligence as a complementary tool that enhances their creative and critical abilities, rather than relying on it as a substitute for these capabilities.

5-Statistical Significance of the Results: The experimental study demonstrated that the computed t-value (3.56) exceeds the critical t-value (2.145) at a significance level of 0.05, indicating a statistically significant difference between the pre-test and post-test scores. Additionally, the correlation coefficient (0.888), which is close to (+1), suggests stability in learners' academic performance after the application of artificial intelligence, reflecting the effectiveness of the educational program in enhancing academic performance.

6-Development of Critical Thinking Skills: The significant improvement in learners' outcomes after using artificial intelligence confirms its effective role in developing critical thinking skills.

Recommendations:

1-Integration of Artificial Intelligence into Educational Curricula: Based on the results, it is recommended to integrate artificial intelligence comprehensively into design education curricula to develop learners' skills in a holistic and adaptive manner.

2-Balancing Technology and Human Element: Emphasis should be placed on achieving a balance between the use of artificial intelligence and maintaining the role of human thought in the educational process to ensure a well-rounded learning environment that fosters creativity and innovation.

3-Training Educational Staff: It is recommended to provide training programs for educational staff to enable them to use artificial intelligence tools effectively in enhancing students' academic performance.

4-Encouraging Critical and Creative Thinking: It is important to encourage students to use artificial intelligence as a tool to enhance critical and creative thinking, while emphasizing their active role in the learning process.

5-Conducting Further Research: It is recommended to conduct additional studies to determine the long-term effects of using artificial intelligence in design education and to evaluate how to achieve an optimal balance between technology and human elements in education.

Conclusion:

The integration of sciences and arts represents a comprehensive approach that can enhance the creative and critical abilities of learners in design fields. Artificial intelligence provides powerful tools that can be used to enhance these abilities, but it cannot fully replace human thought. Educational institutions must be flexible and capable of adopting new teaching methods that align with the rapid changes in technology. Innovation in education requires close collaboration among all stakeholders to ensure the best outcomes.

Declaration of Conflicts of Interest:

The author declares that there are no conflicts of interest to disclose. The research was conducted without any financial or personal conflicts that could have influenced the results or interpretation of the study.

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Generative Adversarial Networks (GANs) are a type of artificial intelligence model consisting of two neural networks that compete with each other: a Generator and a Discriminator. The Generator attempts to create new examples (such as images) that appear realistic, while the Discriminator tries to distinguish between real and fake examples. This technique can be used in design to create new designs, such as patterns, shapes, or even faces, in a way that appears to be human-made.
A group of students with an intermediate level in architectural design was selected, and this group was used as a purposive sample.
To ensure the accuracy of the final results, students were closely monitored by the supervisor to prevent any external assistance, thereby ensuring the integrity and validity of the study. Consequently, a sample of 15 students was selected out of a total of 20 (the entire cohort).
The selected reference group had a specialized instructor who supervised the group during both the first and second projects.

The authors declare no competing interests.

Artificial Intelligence Role in Advancing the Design Education Process

Status:

Version 1

Abstract

Figures

Research Terminology

Introduction

Teaching Design Using Artificial Intelligence

1. The Design Process:

AI-Enhanced Design Process

3- Benefits of AI in Design Education

Applied Study

Experimental Study Methodology

Projects:

Statistical Analysis

Results and Interpretation

t-test:

Pearson's Correlation Coefficient:

Discussion of the Results

Conclusion

Declarations

References

Footnotes

Additional Declarations

Status:

Version 1