Artificial Intelligence in Architecture: An Educational Perspective
Salih Ceylan
a
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
st
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
th
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
a
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
Copyright
c
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
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
th
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
th
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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