Working Toward Advanced Architectural Education: Developing an AI-Based Model to Improve Emotional Intelligence in Education

Abstract

This study explored the integration of emotional intelligence (EI) with artificial intelligence (AI) to address emerging challenges in architectural education. An AI-supported teaching model was developed, utilizing AI tools to assess students’ emotional responses and enabling educators to adapt teaching strategies based on emotional data. This study employed a three-phase methodology: theoretical, analytical, and experimental phases. The theoretical phase involved a comprehensive literature review focusing on the role of EI in architectural education. In the analytical phase, a survey was conducted to evaluate students’ ability to overcome learning challenges using a case study from an Egyptian university. The experimental phase implemented an EI-driven teaching approach with a pilot group of students, incorporating instructor feedback and ChatGPT-4O for assessments in order to minimize potential bias. The results demonstrate that integrating EI into education significantly enhances students’ performance compared to traditional teaching methods. Furthermore, the findings contribute to the development of an AI-based model that provides personalized feedback and fosters a dynamic learning environment, aiming to achieve higher academic and behavioral standards among architecture students. This research offers theoretical and practical insights into advancing the integration of AI and EI in architectural education.

1. Introduction

Intelligence is a multidimensional concept that encompasses several capabilities, such as learning, critical thinking, and problem-solving. Emotional intelligence (EI) is now recognized as a key factor influencing students’ academic performance and cognitive abilities, particularly in the field of architectural education. Emotions and mental reactions significantly shape people’s feelings, thoughts, and behaviors. Creativity is a fundamental aspect of both teaching and learning, and EI plays a critical role in the educational process. EI can be defined as the ability of an individual to identify, comprehend, and regulate emotions effectively within a social context [1]. These emotional traits are essential cognitive skills that enhance problem-solving abilities and creativity within educational frameworks. Various scientific studies have shown that EI can improve students’ social interactions, teamwork skills, and overall academic performance [2]. In architecture, where students often engage in collaborative projects and face stressful challenges, such as meeting deadlines and tackling complex design problems, EI emerges as a crucial determinant of success.

The use of artificial intelligence (AI) in education opens new pathways to creating better learning environments. AI has demonstrated significant potential in personalizing learning experiences and addressing individual student needs. However, despite these advancements, limited research has explored the combined application of EI and AI in architectural education, highlighting a gap in the current literature.

D’Mello and Graesser [3] demonstrated that AI could predict student emotions (e.g., boredom, fluency/engagement, confusion, and frustration) by analyzing dialogues between students and tutors during interactions with an intelligent tutoring system (ITS). Similarly, Arguel [4] argues that AI can detect and assess students’ emotions within interactive digital learning environments (IDLEs) and adapt the environment to meet their needs, potentially improving learning outcomes. AI has also been shown to classify students’ emotions during interactions with immersive environments, enhancing the understanding of their emotional experiences [5]. Furthermore, recent studies, such as AlZu’bi [6], have found that using deep learning methods to detect students’ emotions significantly boosts productivity and enhances the educational process.

Among efforts to integrate AI into emotional management within educational settings, the “Biologically Inspired Cognitive Architecture” (eBICA), developed by Samsonovich [7] is notable. eBICA allows AI to understand and interact with human emotions during social interactions. Similarly, an emotion-based artificial decision-making model has been shown to enhance the performance of educational agents in virtual settings [8]. AI also significantly impacts social emotions such as empathy, compassion, and interpersonal phenomena such as justice and cooperation, which are vital for learning [9]. Moreover, AI can analyze empathic behavior in dynamic social contexts, such as educational settings [10].

This research aims to bridge the gap in the application of EI and AI in architectural education by utilizing ChatGPT-4 to support architectural education. Such an approach is crucial for adapting to the evolving dynamics of education and preparing students for professional environments that require both technical expertise and emotional intelligence.

In summary, this study addresses the following research questions:

• How effective is the application of emotional intelligence techniques in the architectural educational process?
• How effective is the application of ChatGPT-4 in measuring emotional intelligence?

This paper is structured as follows: Section 2 presents the methodology adopted in this research, encompassing theoretical, analytical, and experimental phases. Section 3 reviews the theoretical background and related work, while Section 4 discusses the results and their implications. Finally, Section 5 provides conclusions and recommendations for future research.

2. Methodology

This study adopts a comprehensive methodological framework to explore the integration of emotional intelligence (EI) and artificial intelligence (AI) in architectural education. To ensure repeatability across various academic institutions, this study adopts a pragmatic philosophy, integrating both qualitative and quantitative approaches to achieve the research objectives. This approach ensures a holistic understanding of the phenomenon by combining theoretical insights with empirical data.

Our research is applied with the aim to develop practical solutions for integrating EI and AI in educational settings. It combines exploratory and explanatory methods to investigate the potential impacts of these technologies Figure 1.

The research strategy follows a multi-phase approach:

Theoretical Phase: Conducting a literature review to establish foundational concepts of EI and AI in education.

Analytical Phase: Administering surveys and analyzing data to identify key factors influencing the academic performance of architecture students.

Experimental Phase: Implementing and testing an AI-based model in a controlled education.

Our study employs a cross-sectional time horizon, focusing on data collection and analysis at specific points during the 2024 spring semester to gather relevant insights.

Purposive sampling was used to select participants, ensuring a representative sample of architecture students and instructors. The pilot study involved 13 undergraduate students from the faculty of engineering of an Egyptian university to test the proposed model in a real-world educational context.

• Data Collection Methods

Surveys: Structured questionnaires were distributed to gather quantitative data on students’ challenges and perceptions regarding the integration of EI and AI. The online surveys were accompanied by e-signed consent forms, and, after ethical approval, the university student databases were obtained.

Observations: Classroom interactions were recorded and analyzed using AI tools such as ChatGPT-4 to evaluate emotional responses.

Interviews: Semi-structured interviews with instructors and students provided qualitative insights into the model’s implementation.

• The collected data were analyzed using the following methods:

Quantitative Analysis: Statistical tools were employed to analyze survey results and evaluate patterns in student performance. The research team reviewed the questionnaire to ensure the proper flow of questions. Data collected from the questionnaire survey were analyzed using statistical tools to analyze survey results and evaluate the patterns of student performance.

Qualitative Analysis: A thematic analysis was conducted on interview data to identify recurring themes and insights.

AI-Assisted Analysis: Emotional data derived from AI tools were utilized to provide real-time feedback and validate findings.

The experimental phase was conducted using the following steps:

• Lecture Recording and AI Analysis:

High-quality cameras were employed to precisely record lectures, and we took multiple consecutive photos at regular time intervals during different lectures of the course. These photos were analyzed periodically to assess students’ impressions. The images were uploaded and analyzed using ChatGPT under the emotional intelligence criteria to evaluate students’ personal impressions and behaviors during the lecture. The insights derived from this analysis were then taken into consideration by the lecturer to enhance the teaching experience and address students.

2.

Data Collection and Real-Time Intervention:

Data were collected from multiple lecture sessions and analyzed to identify recurring patterns in student behavior, particularly emotions, to notify the teacher of any significant emotional responses.

• Methodological Limitations

This study acknowledges several limitations:

The sample size was limited to a single institution, which may affect generalizability.

The reliance on AI tools introduces potential biases related to technological accuracy or data interpretation.

The cross-sectional design restricts the ability to analyze the long-term impacts of the proposed model.

Limited resources and infrastructure constrained the scope of the experimental phase.

By addressing these components, the methodology provides a robust framework for understanding the integration of EI and AI in architectural education while ensuring repeatability in other academic contexts.

3. Theoretical Background

At present, the literature includes a wide variety of studies that focus on the vital role that EI plays in improving academic success, social behavior, and personal progress, especially in the field of architecture. These studies pinpoint the necessity for effective emotional awareness and regulation in improving communication skills, motivation, adaptability, teamwork, and stress management. Gardner’s theory of multiple intelligences implies that these findings suggest that intelligence does not merely refer to traditional cognitive measures but includes a wide range of abilities, such as EI [11,12]. Figure 2 presents a summary of the various types of intelligence and their features [13].

Table 1. Types and characteristics of intelligence [11,12].

	Type of Intelligence	Definition	Characteristics
1	Spatial Intelligence	Visualizing shapes and images in space, creating spatial–visual representations.	Imaginative, enjoys drawing and geometry, and good at navigating and identifying directions.
2	Intrapersonal Intelligence	Understanding one’s emotions, goals, and intentions.	Self-aware, goal-orientated, independent, and possesses leadership qualities and strong willpower.
3	Verbal–Linguistic Intelligence	Language proficiency, creating terms, and sensitivity to word nuances.	Passionate about reading/writing, strong memory, quick wit, rich vocabulary, and excels in communication and learning languages.
4	Bodily–Kinesthetic Intelligence	Employing body language to unravel difficulties and express ideas.	Learns through touch and movement, excels in physical activities such as sports, and effective in using body language.
5	Interpersonal–Emotional Intelligence	Recognizing, understanding, and managing emotions in social interactions.	Skilled in understanding and expressing emotions, builds strong relationships, recognizes emotional cues, and regulates personal emotions.
6	Logical/Mathematical Intelligence	Thinking deductively and inductively, recognizing patterns and relationships.	Strong in problem-solving, logical thinking, and handling mathematical and scientific challenges.
7	Musical Intelligence	Appreciating and discerning sounds, melodies, and their emotional impact.	Passionate about music and distinguishing between instruments, quick to retain rhythms and melodies, and focused on surrounding sounds.
8	Naturalist Intelligence	Understanding and enjoying nature, sensitive to natural phenomena.	Enjoys exploring and caring for nature, is sensitive to environmental changes, and is interested in different types of plants and animals.
9	Existential Intelligence	Contemplating philosophical questions related to human existence, life, and death.	Deep thinker, explores the nature of life and personal identity, interested in the spiritual side of life, and is creative in expressing philosophical and existential concepts.

presents a classification of Gardner’s nine types of intelligence into three areas: analytical, introspective, and interactive.

3.1. EI in Architectural Education in Relation to Architectural Organizations

According to McClellan and Conti [12], EI contributes to the development of a better educational experience by raising the quality of social interactions, problem-solving skills, and personal abilities. While traditional education focuses on visual and verbal intelligence, EI emphasizes the importance of developing a supportive learning environment and refining skills such as problem solving and decision making. In addition, it highlights the effectiveness of working in teams and improving the teacher–student relationship in order to permit high-quality educational processes [14].

Teachers can play a pivotal role by adapting teaching methods to meet students’ needs and improve their self-learning by understanding and regulating their emotions. Nonetheless, EI is used with the aim of enhancing cognitive abilities, such as effective interactions, positive communication with others, and the management of negative emotions. Moreover, the ability to understand and manage emotions can have a great influence on people’s lives [15]. Therefore, aspiring to develop both cognitive and emotional skills can contribute greatly to the achievement of a better architectural education. These skills include mathematical and logical abilities, creativity, and effective communication [15,16]. There have been many studies exploring EI in architectural education. These studies can be classified as such below.

3.1.1. Developing Teaching Strategies Based on EI

Viswanathan and Chamba proposed a paradigm shift in architectural education. They suggested substituting traditional lecture-based methods with active learning strategies. This, in turn, highlights the importance of promoting student–teacher interactions and linking theoretical knowledge to practical applications [17]. They argued that the integration of both theoretical and practical pedagogical methods can significantly raise student productivity. This approach plays a pivotal role in striking a balance between intellectual and emotional capabilities. Daniel Goleman emphasized that present society faces several challenges posed by high levels of anxiety, depression, loneliness, and social discord. Hence, Kedar Daniela highlighted the key role of EI in education as an essential factor in improved behaviors, self-efficiency, and a deeper understanding of oneself and others. EI is a key factor in enhancing rational performance, contributing to internal growth and overall wellbeing. By strengthening the application of EI within educational systems, there is significant potential to address and combat these pervasive societal issues. The educational system, therefore, has a crucial responsibility to lay the groundwork for a high quality of life and effective communication from an early age, which, in turn, contributes to the broader goal of global wellbeing [18].

Recently, several studies have emphasized the significance of EI as a key tool for enhancing learning, particularly in areas such as problem solving and collaboration. Further research highlights the impact of using EI in the learning process and its potential to refine educational strategies. In their study, Miguel Sainz, Gil, Cabrero, and Pacheco identified EI as a critical academic competency, advocating for its incorporation into architectural education as a valuable pedagogical approach [19,20]. A study conducted by Shafait and Yuming [21] investigated the influence of EI in self-regulation techniques and indicated a positive correlation between these two approaches. The findings of this study revealed that both academics and administrators can significantly influence creative performance by self-assessing and optimizing these processes. This highlights the critical role of EI in institutions striving for a sustainable competitive environment. This research aligns with the ongoing call to rethink architectural education by focusing on problem-solving skills, personal improvement, critical thinking, and general awareness. It introduces a curriculum design principle and a conceptual framework that employ EI to address the gaps in educational design and better prepare students for the challenges of the modern world [22].

3.1.2. The Impact of Emotions in the Learning Process

Research has explored the role of emotions in shaping students’ comprehension and engagement with architectural educational materials, revealing that emotions play a crucial role in motivating learning and deepening content understanding. Mayer and Salovey (1997) emphasized the significance of EI in education, noting that it enhances students’ ability to filter perspectives creatively. This, in turn, fosters enriched classroom environments conducive to advanced learning [23]. Thus, EI has a substantial influence on the quality of students’ psychological state and significantly improves their design work. Arbel, Murat, and Sizer (2016) found that EI, closely linked to personal traits, has a direct impact on architectural projects and overall outcomes in architectural education. Their research suggests that EI not only affects the design process and the level of the students’ contribution but also influences their susceptibility to burnout, highlighting its importance in both academic and professional contexts within the field of architecture [23].

Recently, numerous studies have explored the connection between EI and interactive thinking with regard to architectural design. Findings have specified a noteworthy correlation between the two. In addition, they suggest that key components of EI such as self-awareness, self-regulation, and self-motivation play a crucial role in enhancing interactive thinking and fostering creative skills. Essential elements, including perception, cognition, and presentation, are critical in the effective application of EI in the design process. Recent research emphasizes that the use of EI considerably contributes to raising the level of awareness, adaptability, and creativity in architecture students, thereby enriching the overall design experience [24].

4. Factors Influencing Architecture Students’ Performance

Recent research has shown that architecture programs are among the most stressful for students. However, student-centered teaching has proven to be widely effective in enhancing competence [25]. The following subsections illustrate the problems that architecture students frequently face:

• Diversity of Courses

Architectural courses are characterized by the inclusion of a wide variety of subjects, such as mathematics, art, technology, and theory, challenging students to understand and apply materials effectively [26], as illustrated in Figure 3 [27].

2.

Time Management Challenges

Architecture students frequently face substantial time constraints due to the demanding nature of multiple tasks and projects, adversely affecting their work quality. These pressures contribute to elevated stress levels and often result in inadequate and irregular sleep patterns. Consequently, these factors can impede students’ overall wellbeing and academic performance, stressing the importance of effective time management strategies and robust support systems within architectural education [28].

3.

Psychological Pressure

Students in architecture programs often experience a great deal of psychological stress due to the high demands of their studies and stringent time constraints. These pressures can have a profound impact on their mental and emotional wellbeing, potentially hindering both their academic performance and overall quality of life. Therefore, better management techniques are required to control these stressors for a more supportive educational environment. This, in turn, contributes to improving both the academic level and mental health of students [28].

4.

Technical Challenges

Architecture students are required to continuously learn and stay proficient in a variety of drawing and 3D design software. ChatGPT-4 supports this learning process by offering interactive lessons, instant assistance, and personalized guidance. Thus, it contributes to enhancing students’ proficiency in tools such as AutoCAD and Revit [29,30]. This integration not only improves their confidence in using essential software but also positively impacts their academic performance and professional preparation in the field of architecture [31,32,33].

5.

Group Work Challenges

Collaborative and team projects in architectural education necessitate effective interaction and coordination among students, which can be particularly challenging in the context of cultural and social diversity [25,31].

The most important skills for successful collaboration include the following:

• Communication skills: Conveying complex ideas effectively through various means [24].
• Problem-solving skills: Analyzing and resolving issues innovatively [25].
• Collaboration skills: Working effectively in teams [25].
• Leadership skills: Motivating and guiding teams toward goals [25].
• Time management skills: Prioritizing tasks to meet deadlines [25].
• Financial challenges: Students face significant financial challenges in covering costs associated with their studies, including tuition fees, textbooks, required software, and project materials [34].
• Physical environment challenges: The physical environment affects student performance and comfort. Enhancing visual aids and classroom facilities can improve learning significantly. Research shows that surrounding conditions and room size significantly impact academic success [28].

6.

Career path influence: Post-graduate students need practical training and support programs to meet job market demands [31,35]. Efficient time and stress management strategies, robust academic support systems, teacher support, effective communication, curriculum reform, and early instruction in resilience and time management skills are crucial [25,36].

Egypt suffers from several problems in architectural education that impact its effectiveness. These problems include the following:

• Curriculum update requirements: Old curricula hinder students’ ability to face new challenges. As a result, the process of active learning is negatively affected, resulting in adverse outcomes [37,38].
• Absence of academic support: Communication problems and time limits result in challenges in providing academic support. Additionally, it is necessary to strike a balance between different architectural perspectives by harnessing professional faculty expertise to support student development [34,38].
• Deficiency in practical training: The lack of experience in the practical application of architectural principles, along with a lack of consistency with industry standards, undermines the practical training students receive [37].
• Technological integration: Currently, modern design technologies, for example, 3D modeling, are significantly important in architectural education. However, they are not optimally utilized [34].
• Low funding and limited infrastructure: Insufficient financial resources and a lack of infrastructure have a negative impact on the quality of education and facilities.
• Student anxiety: Students are usually stressed out, facing several challenges related to their psychological condition and time limits. This requires the presence of a balanced and supportive educational perspective [38].

It is imperative that we find clear solutions to the above-mentioned problems in an attempt to raise the quality of architectural education in Egypt. The Accreditation Board for Engineering and Technology (ABET) identifies a number of educational standards that pinpoint personal skill development, such as teamwork, effective communication, and critical thinking using EI. Consequently, ABET recommends that EI-based curricula be used by accredited programs in all their activities [39]. Quacquarelli Symonds (QS) World University Rankings play a significant role in assessing the quality of global higher education. This indirectly affects EI with regard to teaching quality, student support services, and research environments [40]. The American Institute of Architects (AIA) and RIBA also stress the importance of integrating educational standards that involve using EI skills, such as leadership, empathy, and effective communication, into architectural education. They provide many professional development programs and workshops aimed at improving the use of EI among architects [41,42,43].

5. Pilot Study: Integrating EI into AI to Support Struggling Architecture Students

This pilot study delved into the benefits of integrating EI into AI in supporting architecture students. This study comprised three surveys: the initial survey, the detailed survey, and the evaluation survey. Each survey involved specific phases, objectives, and results, which are explained in this section.

• Initial Survey: Identifying the main challenges faced by students

The survey (1) used a sample of 204 students from the Department of Architecture, Faculty of Engineering, in an Egyptian university. We aimed to identify the main challenges that students encountered in their academic course. The sample had 204 respondents: 120 undergraduate students (40% of the department’s cohort) and 84 recent graduates. They were asked to give comprehensive insight into their academic challenges. Data were collected by means of electronic questionnaires, which made it easier to collect more qualitative and quantitative information.

Table 2. Sample data and rating category values in the survey.

Overall Agreement
	Kappa				Asymptotic 95% Confidence Interval
					Lower Bound	Upper Bound
Overall Agreement	0.086	0.003	24.717	0.000	0.085	0.086
a. Sample data contain 69 effective subjects and 25 raters.
b. Rating category values are case-sensitive.

shows the sample data and rating category values. In the survey, we employed a system of descending order to arrange and rank challenges from the most to the least difficult.

Based on the survey (1), psychological stress was selected by many students as the main challenge that they faced, as shown in Figure 4. The students identified key stressors such as academic workload, career prospects, time management, creativity, criticism, and peer comparisons. These findings underline the importance of mentorship, hands-on support systems, and a nurturing academic environment to lessen these pressures and raise the quality of the educational environment for the students.

The analysis of survey (1) indicates the presence of several significant stressors that adversely affect students’ academic experience. The survey findings acknowledge considerable psychological pressures at the academic and personal levels. These challenges are intensified by certain faculty interaction techniques and a lack of support mechanisms. Furthermore, students struggle to strike a balance between their academic and personal responsibilities. Additionally, technical challenges related to software and technology within the curriculum contribute to student stress and reduce the quality of their educational experience. Career path anxiety also plays a critical role, with concerns about future career prospects generating additional stress and negatively influencing academic performance. These findings highlight the broad effect of psychological pressures on academic achievement. They emphasize the pressing need for further research into teacher–student relationships.

Moreover, they stress the need for the development of targeted interventions to effectively mitigate these challenges and improve student results.

2.

Detailed Survey: Determining the principal causes of psychological stress in students

The second survey examined psychological stresses caused by the student–teacher relationship based on ideas from the initial survey, as shown in Figure 5. This survey was based on feedback from both undergraduate and graduate students. It allowed a thorough examination of the stressors allied with these interactions. The sample was made up of 204 respondents: 112 undergraduate students and 88 new graduates. Including graduates’ viewpoints offers valuable longitudinal insights into how their professional transitions and overall progress are affected by these factors. The survey findings highlight the impact of teacher–student interactions on psychological stress and define the main points that can enrich the educational process and allow support for students. Through this comprehensive analysis, we aim to refine pedagogical strategies and techniques to meet the psychological needs of students.

The survey results highlight both the positive and negative aspects of the student–teacher relationship within the architectural learning environment. They emphasize the key need for a supportive educational experience. On the one hand, 64% of students expressed satisfaction with the continuous support and guidance that they received from their teachers. Additionally, 60% of students acknowledged the constructive feedback and supervision that they received from their teachers, which helped improve their learning experience.

On the other hand, the survey indicates several negative aspects: 68% of the students expressed their dissatisfaction with destructive feedback from teachers that had a negative impact on them. Furthermore, 68% of students suffered from the absence of psychological support from teachers. Moreover, around 68% of students complained that their teachers did not respect their opinions and ideas.

Furthermore, there are other problems, such as a lack of innovative teaching methods; 64% of students wanted more engaging teaching techniques. Moreover, 60% of students lacked the encouragement that they needed to develop their skills, and 52% of students expressed a need for more time with their teachers to discuss and solve their problems. Furthermore, the survey highlights students’ need for equal opportunities to participate in class activities; 60% of students complained about feeling discriminated in class. Moreover, 52% of students expressed their need for more interaction with teachers. The same ratio of students implied that teachers needed to understand their personal problems better and dedicate more effort to helping them improve. These findings emphasize the importance of addressing these challenges in order to help all students improve and complete their educational experience.

3.

Survey (3): Evaluating the EI of instructors on the selected course

In this phase of this study, the link between instructors’ emotional competencies and student success was investigated. Survey (3) assessed instructors’ emotional intelligence (EI) and their capability to encourage their students. It involved 13 students studying the Graduation Project—Architecture (1) course and assessed aspects of the instructor’s EI, including empathy, emotional awareness, and supportive guidance. This stage of this study also integrated AI into EI to enhance the EI of instructors. Thus, it helped improve architecture students’ skills through targeted interactive exercises aimed at enhancing emotional regulation and effective communication through certain actions carried out by the teachers, which included the following:

Recording Lectures and Analyzing Behavior: Teachers used high-quality cameras to record lecture sessions. The recordings were analyzed using AI tools, such as ChatGPT, to assess students’ emotional states (e.g., interest, frustration, happiness). This analysis enabled teachers to identify patterns and adjust their teaching strategies in real time in order to enhance interactions.

Emotional Intelligence Training: Teachers incorporated strategies aligned with the emotional intelligence (EI) framework, such as providing supportive feedback, recognizing individual emotional needs, and employing motivational techniques to foster a positive and engaging learning environment.

The survey results reveal a significant positive impact of emotionally intelligent instructors on students’ educational experiences, underscoring their effectiveness in creating a nurturing and motivating learning environment. Specifically, feedback from 13 students indicated that the instructor demonstrated a profound understanding of, and empathy with, their emotions, and this was observed along with effective communication, attentive listening, and substantial guidance and motivation. These findings emphasize the value of incorporating customized psychological and social support sessions, utilizing a blend of AI tools and emotional intelligence (EI) techniques to face academic challenges such as time management, teamwork, and problem-solving.

The evidence supports the proficiency of this approach in enhancing students’ academic performance and fostering the development of vital life skills, offering potential long-term benefits for their professional and personal growth. Emotionally intelligent instructors significantly influence several key aspects of the educational experience. They play an essential role in developing academic aptitudes through constructive supervision and motivation to direct students toward success. Furthermore, they take part in establishing a positive learning environment that keeps students enthusiastic and engaged.

By addressing students’ emotional needs and building supportive relationships, the instructors managed to enhance self-confidence and encourage active class participation. Their increased interaction with students fosters greater engagement during lessons, while their motivational strategies in helping students allow the development of essential skills and the achievement of their goals. Finally, adopting teaching methods and support strategies based on EI is critical in meeting the diverse needs of students, enabling them to improve their overall educational outcomes and providing a more supportive and useful learning environment.

The comparative analysis of student outcomes comprised the integration of artificial intelligence and emotional intelligence versus traditional teaching methods. The control group in this experiment consisted of the same students. The primary variable was the instructor of the course, who implemented emotional intelligence strategies during the experimental phase. This was achieved by observing the students’ academic performance in similar courses prior to and after the implementation of emotional intelligence techniques. Student grades for the traditional teaching method used in the previous course and those for the Graduation Project (1) course are compared in

Table 3. Grades of students on both courses. Source: the authors.

Student Number	Grades on the Previous Course Taught Using Traditional Methods	Course Points	Grades from the Course Taught Using EI–AI Mechanisms	Course Points
Student 1	−C	1.7	B	3
Student 2	+B	3.3	A−	3.7
Student 3	−B	2.7	B+	3.3
Student 4	D	3	A−	3.7
Student 5	+D	1.3	B+	3.3
Student 6	D	2.7	B	3
Student 7	+B	3.3	B+	3.3
Student 8	D	1	C−	1.7
Student 9	D	1	B−	2.7
Student 10	C	2	C	3.3
Student 11	+D	1.3	B	3
Student 12	B−	2.7	B+	3.3
Student 13	D	1	B	3

. Figure 6 illustrates the grades of students from both the traditional teaching course and the AI–EI-integrated course.

6. Results

The comparative analysis between the previous course taught using traditional methods and the Graduation Project (1) course employing emotional intelligence (EI) methods pinpoints considerable advancement in the performance of students and a higher level of satisfaction regarding the effectiveness of the learning process.

In the previous course, which utilized traditional teaching methods, student grades displayed considerable variability, ranging from D (1.0) to B+ (3.3). Many students received lower grades, reflecting inconsistent performance. Complaints frequently highlighted issues such as inadequate communication, limited interactions with the instructor, and a perceived lack of support. These factors were found to deter students from engaging and reduce the benefits of the educational environment.

In contrast, the Graduation Project (1) course, which incorporated EI methods, showed a marked enhancement in student performance. The grades achieved on this course ranged from B (3.0) to A− (3.7), with most students achieving significantly higher grades compared to the previous course. Students expressed appreciation for motivating sessions that fostered positive interaction and the exchange of ideas. The supportive environment facilitated open expressions of thoughts and emotions, which increased the effectiveness of the educational process. Overall, the integration of EI methods resulted in increased student engagement, higher levels of participation, and improved academic outcomes.

However, several challenges and obstacles were identified in the implementation of the AI-based model for EI enhancement. The presence of a camera recording student behavior during lectures causes a lack of privacy. Although this recording is considered necessary for secure data processing, it poses challenges. For example, technical problems such as high-quality cameras and advanced AI software can be costly. Moreover, the occurrence of technical failure in terms of AI accuracy would adversely affect the consistency of the analysis. Another problem that can hinder the development of education is a lack of desire on the part of both students and teachers to start to change. In addition, comprehensive training and continuous support are essential in enabling them to use AI tools effectively. Additionally, teachers can become distracted due to continuous AI analysis and the high costs of cameras, AI software, and infrastructure.

These findings show the advantages of using EI methods in architectural education. They stress the benefits of employing such technology. The present study sheds light on the significance of understanding these problems and obstacles when implementing AI in an attempt to improve the architectural learning environment.

7. The Developed Model: Integrating AI for Enhanced EI

Figure 7 shows the AI-based model developed by the authors. This model aims to integrate emotional intelligence (EI) into architectural education. It is designed to help teachers keep an eye on each individual student and monitor their engagement during lessons. It achieves this by analyzing the emotional responses of the students and providing instant feedback.

Table 4. Steps in the developed proposal. Source: the authors.

	Stage	Description
1	Start	The teacher starts up the application.
2	User Login	The teacher logs into the application.
3	Select Course/Session	The teacher selects the appropriate course or session.
4	Camera Setup	The teacher checks the functionality of the camera
5	Select Time Interval	The teacher designates time intervals for capturing images in the class.
6	Confirm Settings	The teacher checks all session settings.
7	Start Image Capture	The application captures images at the intended intervals.
8	Image Processing	The captured images are analyzed using AI.
9	Emotional Analysis	An EI analysis is conducted by AI based on the images processed.
10	Store Data	The data and images are securely stored.
11	Send Notification to Teacher	When the student focus levels drop below 70%, follow-up notifications are sent to the teacher.
12	Generate Alerts	When the session ends, alerts go off, indicating the need for action.
13	Send Notification to Teacher and Create Survey Room	If the session is running and students are focused, notifications are sent to the teacher, and a survey room is created.
14	Send Notification to Teacher and Create Survey Room	If the session is running, but students are not focused, suggestions for modifying the teaching method are given, and a survey room is created.
15	Send Survey to Students	The survey is sent to the students for feedback.
16	Collect Survey Responses	Student responses to the survey are gathered.
17	Store Survey Data	The survey data are securely stored.
18	Generate Final Report	A final report is generated to summarize the session.
19	Include Emotional Analysis and Survey Results	The report covers both emotional analysis and survey results.
20	Display or Export Report	The report can be presented or exported for further analysis.
21	Session Summary	A summary of the session is given to the teacher to review.
22	Save Session Data	All session data are saved to be used for future reference.
23	Log Out	The teacher logs out of the application.
24	End	The session comes to an end.

shows the application, which can be run on iOS, Android, and desktop platforms, utilizing either built-in or external cameras to capture images for the students inside the classroom. The AI model relies on analyzing facial expressions and body language to estimate the level of student engagement and alertness in class. At fixed intervals, the app sends a summary to the teacher, giving them suggestions on how to raise the quality of teaching. Additionally, teachers can receive a statistical report at the end of their class. This report may provide graphs and images showing the level of student engagement in the class.

These reports can also be exported in a PDF file. Furthermore, the app provides feedback about the student performance in the classroom through a virtual survey room. The results can be automatically analyzed and presented to the teacher.

The flowchart can be summarized as follows:

(Start) -> (User Login) -> (Select Course/Session) -> (Camera Setup) -> (Select Time Interval) -> (Confirm Settings) -> (Start Image Capture) -> (Image Processing) -> (Emotional Analysis) -> (Store Data) -> (Check Time Interval) -> (Generate Alerts) -> (Send Notification to Teacher) -> (Create Survey Room) -> (Send Survey to Students) -> (Collect Survey Responses) -> (Store Survey Data) -> (Generate Final Report) -> (Include Emotional Analysis and Survey Results) -> (Display or Export Report) -> (Session Summary) -> (Save Session Data) -> (Log Out) -> (End).

8. Discussion

Our study explored the integration of emotional intelligence (EI) and artificial intelligence (AI) in architectural education to enhance student performance and proved that students exposed to EI–AI-integrated teaching methods demonstrated significant improvements in their grades compared to traditional methods. The emotionally intelligent instructor contributed to a supportive and engaging learning environment, which fostered improved student motivation and engagement.

The use of AI tools such as ChatGPT-4 provided real-time emotional feedback and personalized guidance, addressing students’ cognitive and emotional needs.

Our pilot study revealed the potential of EI–AI strategies in supporting academically struggling students in overcoming challenges related to time management, teamwork, and psychological stress. These findings align with the existing literature emphasizing the role of EI in education. Studies such as Goleman (2001) [1] and Mayer (2004) [2] highlight the importance of emotional intelligence in fostering creativity, problem-solving skills, and collaboration, particularly in disciplines such as architecture. The observed improvement in academic performance supports the conclusions of Ahmad et al. (2023) [29] and Maghool et al. (2018) [32], who suggest that AI tools can enhance technical proficiency and engagement among students.

Furthermore, this study’s results resonate with the research carried out by D’Mello and Graesser (2012) [3], which demonstrated the ability of AI systems to predict and address students’ emotional states. Similarly, the use of AI for real-time emotional feedback, as implemented in this study, corroborates the findings of Rodríguez et al. (2020) [5], who highlighted the potential of immersive AI tools in enhancing learning experiences.

However, this study extends the literature by demonstrating the combined use of EI and AI in a practical educational context, emphasizing their synergistic impact on improving student outcomes. This integration represents a novel approach to architectural education, addressing a gap in existing research.

• Research Limitations

While the findings of this study are promising, several limitations must be acknowledged:

Sample size: This pilot study involved a relatively small sample of 13 students, which limits the generalizability of the results. Future studies should include larger and more diverse samples.

Short-term focus: This study focused on a single course and did not assess the long-term impact of EI–AI strategies on student outcomes.

Instructor variability: The results may be influenced by the specific instructor’s application of EI techniques, which could introduce variability in future implementations.

Technological dependency: The reliance on AI tools introduces potential challenges related to technological accuracy and accessibility, which could affect broader adoption.

Future studies should be carried out to address these limitations, and they should include the following:

Longitudinal studies should be conducted to assess the sustained impact of EI–AI integration on student outcomes.

The sample size should be expanded to include students from diverse academic and cultural backgrounds.

The scalability of EI–AI strategies in different educational settings and disciplines should be explored.

The role of faculty training in enhancing the effective implementation of EI techniques should be investigated.

9. Conclusions

In conclusion, the integration of emotional intelligence and artificial intelligence offers a promising avenue for transforming architectural education. By addressing students’ cognitive and emotional needs, these strategies can enhance academic performance, foster essential life skills, and prepare students for future professional challenges.

In our study, we aimed to examine the benefits of using EI in teaching. It was proven to significantly improve students’ academic performance in contrast to traditional teaching methods. Students who had been suffering from several obstacles managed to achieve higher grades and a noticeable improvement. Thus, based on these results, it is obvious that EI plays a pivotal role in improving student engagement and learning outcomes. Therefore, we recommend utilizing AI as a means to collect emotional data through facial expressions and body language analysis in order to support educators in architectural education. We propose the idea of using instant feedback systems to keep an eye on students and enable them to handle their emotions properly. Our results highlight the necessity of using adaptive teaching methods based on emotional insights. Additionally, feedback analysis can provide personalized emotional support for each student based on their needs. Nonetheless, ensuring continuous improvement means conducting an assessment of the effect of using these AI-driven EI strategies on students’ level of participation and classwork. These approaches are intended to establish a more adaptive, responsive, and supportive educational environment that can contribute significantly to student success.

10. Recommendations

This research supports the application of EI to online education platforms in order to encourage live student–teacher interactions. This, in turn, will enhance student engagement and provide emotional support for students within learning environments. Additionally, our results call for the creation of sustainable development programs that integrate EI into the architectural curriculum. To further support this initiative, we recommend offering training workshops to develop EI skills through the application of emotionally responsive teaching methods. Finally, we propose utilizing EI through case studies and practical examples to emphasize the importance of emotional considerations in design processes. To conclude, through these recommendations, we aim to improve educational experiences and advocate for a more emotionally intelligent perspective on architecture.