Artificial Intelligence in Architecture: An Educational Perspective

Salih Ceylan

Faculty of Architecture and Design, Bahçeşehir University, Ihlamur Yildiz Street No: 10, Beşiktaş, Istanbul, Turkey

Keywords: Artificial Intelligence, Architectural Design, Architectural Education, Architectural Representation.

Abstract: Artificial intelligence is a phenomenon that currently influences every aspect of life. AI applications already

started to change the business methods in different disciplines. Architecture is one of the disciplines that is

highly affected by the developments in AI technologies. The complex nature of the practice makes

architecture a significant area of experiment for artificial intelligence applications. From building information

modelling to advanced visualization techniques, artificial intelligence and architecture’s collaboration has

important outcomes that affect the practice’s present and future. However, the permanent and more

fundamental effects of AI on architecture must be followed in the architectural education curricula which

provides the basics for the future of the profession. This paper presents a study that reviews the methods of

artificial intelligence in architecture from an educational perspective. It includes existing implementations

and potential future strategies from different domains and areas of theory and practice that might be useful

for the development of architectural education.

1 INTRODUCTION

Artificial intelligence is one of the most popular fields

of study in the 21

century. Although its hypothetical

roots may be found in the ancient history, its real

appearance in the world agenda was seen at the end

of the 20

century. Various disciplines such as

engineering, medicine, marketing, economy, etc.

approach AI as a supporting and influencing force for

their innovative work. It is clear that AI will somehow

become a part of daily life in the future, but important

is the question about which role it will play, and how

AI systems and human civilization will coexist

(Haenlein and Kaplan, 2019).

Artificial intelligence is defined as a system’s

ability to interpret external data correctly, to learn

from such data, and to use those learnings to achieve

specific goals and tasks through flexible adaptation

(Kaplan and Haenlein, 2019). Obviously, the

definition is based on the term “data”. Therefore, AI

is growing parallel to the developments in computing

power and the amount of data stored in global

resources. Every discipline is trying to utilize and

interpret the data from its own perspective. AI makes

self-driving cars, distant operations, image

recognition, or smart homes possible through relevant

https://orcid.org/0000-0003-3808-7773

algorithms. Consequently, one may assert that AI is a

tool for any discipline to redefine itself.

Architecture is the discipline that is responsible

for the design of all built environment. It provides an

harmonic assembly of interdependent elements, so

that it needs to make use of any possible support to

improve itself, technology being one of them. Thanks

to technology, design became a prescriptive activity,

in which models and drawings are used to foresee

reality, and in which everything must be resolved

before the construction process (Celani, 2012). It also

helps complex design objectives and forms to be

fulfilled. To ensure that, architects utilize the

opportunities given by artificial intelligence (Bhatt et

al., 2016). Therefore, it is one of the disciplines that

may redefine itself through the novelties brought by

artificial intelligence. Accordingly, following

questions emerge: How can architecture transform

data and algorithms into useful instruments for the

development of the practice? What are the methods

that can be utilized for effective use of AI in

architecture? How can AI become an essential part of

architecture?

The answers for these questions partially lie under

some existing and potential technologies such as BIM

software, building physics analysis tools, and

100

Ceylan, S.

Artiﬁcial Intelligence in Architecture: An Educational Perspective.

DOI: 10.5220/0010444501000107

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 1, pages 100-107

ISBN: 978-989-758-502-9

parametric design interfaces. In addition to these and

more importantly, for a permanent existence of AI in

architecture discipline and a firm coordination

between them, a fundamental understanding must be

provided that considers AI as a basic and natural

element of architectural education. Since the recent

increasing computer-based applications, the future of

architectural education has been at the forefront of

architectural debate (Güney, 2014). This paper

presents a study on the connection of AI and

architecture, looking into their existing common

points and potential collaboration options. In the focal

point of the study there is architectural education and

its various aspects from an open perspective for the

influence of AI. The paper also presents in which

domains of architectural education AI shall be

integrated, and what kind of strategies must be

followed for an effective implementation. The second

chapter of the paper presents the existing relationship

between the architecture practice and AI, as the third

chapter is focused on the educational perspective of

AI in architecture. Fourth chapter recommends

strategies for the implementation of AI into

architectural education curriculum, as the fifth and

final chapter concludes the paper with discussions on

positive and negative aspects of AI in architectural

education and suggestions for future studies.

2 AI IN ARCHITECTURE

Walter Gropius (1970) asserted in his book Scope of

Total Architecture that good architecture should be a

projection of life itself that implies an intimate

knowledge of biological, social, technical, and

artistic problems. Based on this statement, it is

obvious that architecture is a complex practice that

needs to take various factors into consideration. In the

pre-industrialization period, the amount of the data

for the architects to deal with during the design and

construction periods was within the limits for a

human being to process; mostly based on user needs,

local considerations, and construction methods.

However, in the 20

century, due to globalization and

the developments in the computational technologies,

the amount of data to deal with has increased

drastically. Cristopher Alexander (1964) stated more

and more problems are reaching insoluble levels of

complexity and at the same time they also change

faster than before, so that new approaches including

technological solutions need to be employed in

architecture. Accordingly, attempts to combine

computers and architecture increased in the second

half of the 20

century. As the process of employing

computational technologies with architectural

purposes accelerated, the amount of data to deal with

also increased proportionally. The amount of data

produced globally between 2010 and 2019 raised

from 2 to 41 zettabytes, more than 20 times; and it is

projected it will raise up to 150 zettabytes, another 4

times in the next 5 years (Holst, 2020). A significant

portion of this data is about built environment and

human activities which are areas of interest for

architecture. Consequently, architecture is in

desperate need of support from computational

technologies and artificial intelligent.

The contribution of artificial intelligence on

architecture can appear on different aspects. Firstly,

AI provides architecture an enormous amount of data

and processing speed to create analytical information

that have significant influence on decisions in any

phase of design. Secondly, computer-aided design

(CAD) programs and algorithmic or parametric

design tools can generate forms that could not exist

without computation (Steenson, 2018). Moreover, AI

makes fast, effective, and alternative methods for

visualization and prototype production possible.

Building information modelling (BIM) software aid

architects to handle design and construction stages as

a holistic process. Finally, AI also augments

architecture in the production phases with automated

construction opportunities. Next chapter of this paper

presents the ways of AI influencing architecture from

its different aspects and in different phases of design

and construction.

2.1 Data Collection and Processing

AI is all about data processing and it has reached an

enormous amount and speed nowadays. Accordingly,

architecture practice gets its share from this

development. For the initial phases of design, there is

a big amount of data for the architects to process:

Legal codes, physical environment analysis, user’s

needs, functional requirements, previous cases, etc.

These are all about data that needs to be processed.

AI provides significant support dealing with all this

data that may be impossible to process without the

contribution of computational technologies.

Additionally, reducing the time for indexing and

classification of information required for the starting

phases of design has an unnoticed but important

effect on the design process.

2.2 AI to Create Design Options

Commercial software on the use of AI in architecture

have reached an advanced level already. Some of the

Artiﬁcial Intelligence in Architecture: An Educational Perspective

101

products work as standalone software as others

function as plugins in other more advanced programs.

One of them, Dynamo is a plugin for Revit and

enables users to use Virtual Programming to process

data and compose custom algorithms (Mousiadis and

Mengana, 2016). It gives the user the ability to

automate processes with the logic of a graphic

algorithm editor (Sandzhiev et al., 2018). Likewise,

CATIA, which stands for Computer Aided Three-

Dimensional Interactive Application, was first

produced in 1977 and is still being used as an

algorithmic design application. It proves the

relevance and coherence of the new technologies,

materials, machinery, progressive methods and

information tools that enable more efficient use

materials (Dubovska et al., 2014). The software is

being used by famous architectural offices

worldwide. Another one is the graphical parametric

form generationg tool Grasshopper, which works

under Rhinoceros. It is being used in different scales

and aspects of design. For instance, Schneider and his

colleagues (2011) utilized Grasshopper for the

development of an urban design proposal at a

teaching exercise.

In addition to the products already in the market,

researchers and academics keep studying on the

methods to combine AI and architecture with each

other in terms of design development (Marson and

Musse, 2010; Mohammadi et al., 2018; Ahmed et al.,

2012; Jabi et al., 2017). For instance, Chaillou (2019)

applies AI to floor plan analysis and generation with

the three-fold aim of generating floor plans,

qualifying them, and to allow users to browse through

the options. Another important example of studies is

the Project Discover, which is an application of

generative design for space planning (Nagy et al.,

2017). Another one is DANIEL, a deep architecture

for automatic analysis and retrieval of building floor

plans (Sharma et al., 2017). In another study, As et al.

(2018) demonstrate how to use generative adversarial

networks (GANs) to generate unique and original

design variations.

2.3 Building Information Modelling

Building information modelling (BIM) is one of the

most popular terms in the world of contemporary

architecture. BIM is also a ground-breaking

development as it is a multidisciplinary approach and

contributes to strengthen the relationship between

different participants of the construction industry

such as architects, engineers, and contractors.

According to Eastman et al. (2008), BIM is not only

a technology change but also a process change. Main

aims of BIM are; to decrease project cost, increase

productivity and quality, and reduce project delivery

time (Azhar, 2011). The term BIM refers to a

simulation of the building design in virtual

environment. The resulting model, a Building

Information Model, is a data-rich, object-oriented,

intelligent and parametric digital representation of the

facility, from which views and data appropriate to

various users’ needs can be extracted and analysed to

generate information that can be used to make

decisions and to improve the process of delivering the

facility (Azhar et al., 2008). Therefore, regarding the

extreme complexity of the multi-layered and multi-

disciplinary structure of BIM software, AI has to be

its backbone.

2.4 Building Performance Analysis

Building performance analysis refers to the process

where the building’s behaviour under certain effects

is inspected. It is not a new term, but with the support

of AI and BIM, it has become easier to conduct, more

reliable, and reproducible. Nowadays, software or

plugins specialized in performance analysis are being

used through interoperable processes with BIM

software for more effective and productive results.

Two main types of building performance analysis

come forward: energy performance and structural

performance. Both are important from different

perspectives. The next subchapters of the paper are

about the two types of analysis made possible using

AI.

2.4.1 Energy Performance Analysis

Energy performance analysis (EPA) can be

conducted on various areas of a building with

different focal points such as visual and thermal

comfort on façade of buildings (Naboni, 2014;

Marroquin et al., 2014). Software for EPA are

evaluated through following criteria: Usability and

information management of interface, integration of

intelligent design knowledge-base, interoperability of

building modelling, and the accuracy of the tool and

its ability to simulate complex and detailed building

components (Attia et al., 2009). Currently, there are

many EPA tools that are accessible such as Ladybug,

Honeybee, Geco, and Heliotrope-Solar. They mostly

operate as plugins under other software. For instance,

Ladybug is a tool to work in collaboration with

Grasshopper, with an effort to support the full range

of environmental analysis in a single parametric

platform (Roudsari and Pak, 2013).

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2.4.2 Structural Performance Analysis

Structural performance analysis is a domain in the

intersection of architecture and engineering. It is an

extremely important issue and its training is

sophisticated and interdisciplinary. Therefore, its

education must be given properly to avoid disasters

caused by wrong or poorly executed structures in the

future.

2.5 AI in Architectural Representation

Representation has been a part of architectural design

since the first days of the practice. Nowadays, in CAD

applications, every design tool has an interface for

representation. However, representation has gone to a

very advanced level under the influence of

computational technologies and AI, providing

alternative media for visualization. s an important

means of architectural expression, the virtual

technology of architectural animation provides a new

digital mode for the promotion of architectural image

(Yang, 2020). There are software and tools that

provide a high quality of visual products with the help

of AI, such as Vray which works under several

different programs. Additionally, some software

Lumion, Twinmotion, and Cinema4d make

animations with realistic renderings very easy to

produce and edit. Dounas (2020) sees animation as a

computational understanding of architectural design.

With the software, it is even possible to simulate the

appearance of the architectural product under

different weather conditions like snow or rain.

Another novel representation method emerged

with virtual reality (VR) and augmented reality (AR)

in architecture as interactive techniques. Interactive

visualization of architecture provides a way to see

current, as well as future stances of buildings

(Aliaga,2007). Some existing programs added AR or

VR options to its visualization methods through

plugins like Enscape or VRSketch, as some other

software such as EyecadVR and IrisVR completely

focus on VR experience as a representation method

for architectural products.

2.6 AI in Construction

Construction industry has evolved from traditional to

modern methods of construction (Rohani et al.,

2014). Therefore, it could use the support of AI

effectively through collaboration with robotics, BIM

and AR technologies. One of the ways AI supports

construction is mostly using additive manufacturing.

Additive manufacturing or 3dprinting nowadays is a

novel but effective way of constructing buildings. It

was in the past an expensive process, but recently it

has become one of the most cheap, affordable, and

eco-friendly ways of constructing buildings (Mathur,

2016). Additionally, automation is an important

innovation in construction. Systems like automated

building construction, shuttrise, and steel frame

remote releasing are examples of automated

construction (Maeda, 2005). According to Wang and

Love (2012) industrialization of the construction

process requires a high level of automation, which

happens to the site work tasks that require high

integration of information and physical intensive

resources.

3 AI IN ARCHITECTURAL

EDUCATION

Architectural education forms the basis of the

practice. Therefore, it must be up to date regarding

the developments and requirements in the field of

architecture. As the architecture profession is under

the strong influence of technological developments,

its education should also be following them and

update itself accordingly. Accordingly, artificial

intelligence as an effective factor in architecture

needs to be a part of architectural education.

However, as mentioned in the previous chapters,

architecture and AI have a multifaceted and

complicated relationship. Additionally, architectural

education has a very complex and dynamic structure.

Therefore, the implementation of AI into architectural

education needs a systematic approach. Various

researchers and educators have begun to address the

need to integrate digital design in architectural design

education (Oxman, 2008). The need must be well

defined and met with a holistic organization that

handles the whole architectural education curriculum.

AI tools are already implemented in many parts of

educational process including content development,

teaching methods, student assessment, and

communication between teacher and students

(Chassignol et al., 2018). However, especially for

architectural education more effective actions are

required. Implementation of AI into architectural

education is also important for stronger and healthier

relationship between architecture and AI.

Architectural education as the foundation of the

profession is responsible for equipping young

architects with proper tools to tackle emerging

problems of society (Ceylan, 2020). If the upcoming

generations of architects are aware of the

Artiﬁcial Intelligence in Architecture: An Educational Perspective

103

opportunities provided by and well equipped with the

skills to utilize AI in the design and construction

processes; they will contribute to the relationship

between the profession and AI. The next chapters of

the paper examine possible strategies and methods for

the implementation of AI into architectural education.

3.1 Strategies for the Implementation

of AI into Architectural Education

Architectural education has a complex structure with

various modules. Every module has a different

purpose and characteristics. In the core of the

education there is the design studio. All other courses’

knowledge is conveyed on the design studio where

the students can reflect their educational gains and

improve their design skills. The architectural

curriculum is composed of fundamental courses that

develop design knowledge; courses that develop the

scientific formation of architecture; courses for

strengthening architectural representation; and design

courses, a combination of the others and constitute the

most crucial part of design education (Demirbaş &

Demirkan, 2003). In this paper, this four-fold

definition of architectural education is used as the

basis of the proposed strategies.

The complex structure of architectural education

together with the multi-layered influence of AI on

architecture requires a holistic and well-organized

approach for the implementation of AI into

architectural education. Three modules of education;

theory, technical, and representation modules, in

addition to the design studio must be considered

separately in detail, but at the same time in harmony

with each other. The next subchapters present the

strategies and methods for the implementation of AI

into each module of architectural education.

3.1.1 Theory Module

Theory module in the architectural education

curriculum is the part where the intellectual

foundations of architecture students’ knowledge are

laid. It mostly consists of courses for the history of

architecture, architectural theories, and some courses

about legal and ethical aspects of architecture. At the

same time, it is the least affected module by the

developments in AI technologies for now. However,

there is still some potential contribution of AI for the

theoretical courses by providing data collection,

storage, and processing. The lectures in the

theoretical courses can use information from the

architecture history, sample cases, and literature

review organized and indexed with AI tools.

Educators or students can choose from previously

composed information whichever is more useful and

necessary for them.

However, in the future the effect of AI on

theoretical courses might be much more significant.

It would be possible that the theoretical classes are

given by AI sources, without the need to human

lecturers. Information stored in the database can be

organized and presented to the students through an

interface that works automatically. Thus, regarding

theoretical courses, someday, AI may be able to do all

the work a lecturer can do.

3.1.2 Technical Module

Courses in the technical module equip architecture

students with the knowledge and skills that they need

to practice the profession. Building materials and

methods, structure, environmental control systems,

professional practice, and conservation are among the

courses taught in this module.

AI can influence the courses in this module most

effectively through BIM software and building

performance analysis tools. Additionally, mapping

and photogrammetry software like Pix4d or similar

tools can be used to perform photogrammetric

processing of digital images to generate 3D spatial

data for construction and conservation purposes.

One of the biggest benefits that BIM tools can

provide for the students of architecture is that they can

introduce building elements effectively through their

continuously evolving and expanding libraries.

Thousands of elements in the library are ready for the

use and examination of the students in their studio or

homework. Additionally, BIM software can also help

teaching the student how to use information in an

effective way without getting into too many details by

only knowing how information is accessible.

Building performance analysis tools can also be

beneficial for the students, especially by raising

awareness among them on the importance of a

building’s energy and structural performance.

Students who can access energy and structural

performance information of buildings quickly with

the support of AI may become eager to get further and

obtain more important and useful information on how

to make it even more energy efficient or more

resilient.

The benefits of BIM and other AI applications in

technical module of architectural education increases

when they are successfully connected with other

modules of education, especially representation

module and the design studio.

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3.1.3 Representation Module

The representation module is the place where students

are introduced to various methods to reflect their

design ideas on different media. It is the module that

might potentially be the most affected by the

developments in AI technologies. With the support of

AI, courses for representation methods in architecture

may become something more than only teaching how

to use digital technologies and computers as drafting

tools, they may become real design instruments.

According to Chaillou (2019), the machine, once the

extension of our pencil, can today be leveraged to

map architectural knowledge, and trained to assist us

in creating viable design options. Courses in the

representation module of architectural education are

the most suitable place for this paradigm shift to

happen.

Parametric and algorithmic design is already on

the agenda of architecture and it has somewhat a place

in the architectural education curriculum. However,

the attempts to put these emerging design tools are

based on the personal efforts and dedication of

educators and students. An institutional approach is

needed for these methods to be effective parts of the

curriculum. Parametric and algorithmic design

methods that are influenced by AI need to be

compulsory rather than elective courses for every

student to be aware of the importance of these design

methods. Being proficient to use these tools, students

may direct their energy and interest to more effective

aspects of design.

Additionally, AI also affects representation

through novel methods like VR, AR, and 3d printing.

Students need to be aware of these methods for digital

communication to create more impressive

presentations. Alternative realities also help students

to experience their designs in 1 to 1 scale and more

intimately, so that they can initialize their design

approach better. 3d printing tools help students with

their physical models by enabling them to bravely try

complex forms without the suspicion to materialize

the physical model.

In brief, AI may have revolutionary effects on the

representation module of architectural education if

implemented properly. A holistic model needs to be

applied for all the tools to function in harmony and be

beneficial for the students. The place where the

proposed methods and strategies can be tested is the

design studio as it is in the centre of architectural

education.

3.1.4 Design Studio

Design studio is the core of architectural education. It

is the place where all theoretical and technical

knowledge come together in exercises for the

development of students’ design skills. In the design

studio, students learn a new language, new skills like

visualization and representation, and architectural

thinking as different aspects of design education

(Ledewitz, 1985). Accordingly, it is the place where

the effects of AI on architectural education

curriculum can be recognized most evidently.

All stages of design in the studio may be

supported by AI in various ways: In the starting

phases where the student research about the given

subject, they can benefit from AI in terms of data

processing and indexing of research objects. They can

explore databases to find relevant information for

their design subject. Likewise, they can use similar

methods in the environmental analysis phase. In the

preliminary design phase, they can use parametric

design or algorithmic design tools for layout

decisions or façade design. In the following phases,

when they have some design proposals already, they

can use building performance analysis tools to see

how their proposed buildings perform in terms of

energy efficiency and structure. They can also benefit

from VR environment to experience their design in

full scale and free from the rules of the physical world

and use alternative realities for the presentation of

their ideas. 3d printing tools can be utilized when

making physical models for more precise products,

even if they have a complex form. Thus, AI can touch

a student’s design journey in the studio from multiple

aspects through various instruments. This gives the

student the opportunity to focus on more important

points in design for more successful design process,

as well as final products.

4 CONCLUSIONS

The connection between AI and architecture is

undeniable. Many different fields of work prove that

AI has a lot to offer for architecture. If architects can

use the opportunity to utilize AI in various phases of

design and construction, the nature of the profession

will change irreversibly. The advent of AI in

Architecture is still in its early days but offers

promising results (Chaillou, 2019). However,

nowadays it is too early to speak about a fundamental

change because the relationship between AI and

architecture is superficial; it does not come from the

ground. For a stronger relationship and change in the

Artiﬁcial Intelligence in Architecture: An Educational Perspective

105

basic understanding of the profession, the form of

architectural education needs to be calibrated towards

the utilization of AI. However, the road is long, and

educators must progress carefully so that the process

of the implementation works properly. Rather than

personal and discontinued efforts, a holistic and

systematic approach is needed.

Architects and architecture students need to

remember that except its advanced levels of

complexity and numerous alternatives it offers, AI is

still a tool for design and the architect is the designer.

It only processes the data according to defined

algorithms. AI does not understand context, or design

in historical context is an overcomplicated issue for

AI. Human mind is still the final decision maker for

design issues. Creativity and intuition remain as

genuine features of the human mind. Designers must

learn how to utilize AI tools rather than considering

them as the main actors of design.

The relationship between AI and architecture is

very strong. The educational aspects are also very

important and complicated. For more concrete results

and proposals for future changes in architectural

education towards the use of AI, more scientific

research with statistical outcomes, as well as case

studies must be conducted. The future holds great

potential for AI and architecture collaboration if it is

managed properly.

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