AI Literacy for Cultural and Design Studies

Sophie Schauer

and Katharina Simbeck

School of Computing, Communication and Business, HTW University of Applied Sciences,

Wilhelminenhofstraße 75A, 12459 Berlin, Germany

Keywords:

Artiﬁcial Intelligence, Cultural Sciences, Design, Higher Education Institutes, AI Literacy.

Abstract:

Artiﬁcial Intelligence (AI) is applied to an extending number of academic ﬁelds, including culture and design.

Hence, there is a necessity to incorporate AI competencies in the curricula of cultural and design studies. This

paper explores potential points of contact between cultural and design studies, AI technologies, as well as rel-

evant competencies. Through a comprehensive curriculum analysis of two study programs, expert interviews

and analysis of sample projects, the paper ﬁnds practical connections for the inclusion of AI competencies into

culture and design studies. We propose components of AI literacy for students in culture and design programs

along the categories of technical understanding, critical appraisal and practical application. The ﬁndings con-

tribute to the ongoing discourse on the interdisciplinary use of AI, offering insights into the evolving skill set

in the ﬁeld of culture and design.

1 INTRODUCTION

Artiﬁcial Intelligence has impacted various aspects

of our daily lives, and its signiﬁcance is continually

growing. In response to this inﬂuence, incorporating

a fundamental understanding of AI has become in-

tegral to Higher Education Institutions’ (HEIs) cur-

ricula. This necessity goes far beyond courses in

computer science and extends into nearly every ﬁeld

of study (Ng et al., 2021). The transformative po-

tential of AI has become apparent, affecting indus-

tries, governance, healthcare, arts, and countless other

domains. Hence, HEIs recognise the importance of

equipping students across diverse disciplines with AI

competencies (Laato et al., 2020).

Culture and design, in particular, have experi-

enced a rise in the relevance of AI competencies

(Sang

uesa and Guersenzvaig, 2019). AI technolo-

gies can aid the analysis of cultural artefacts and his-

torical documents and even support the preservation

and reconstruction of cultural heritage (CH) (Pavlidis,

2022). The same applies to design studies that use

generative AI as a supportive tool during their cre-

ative thinking process (Verheijden and Funk, 2023).

The use of AI technologies is, however, related to sev-

eral issues. AI systems have been found to replicate

biases (Ntoutsi et al., 2020) and machine learning sys-

https://orcid.org/0009-0006-3350-7803

https://orcid.org/0000-0001-6792-461X

tems require training on large data sets which leads to

ethical and copyright issues (Mehrabi et al., 2021).

As AI continues to evolve and reshape our world,

the collaboration between AI, culture and design is

set to bring forth novel approaches to understanding

and preserving our cultural diversity and innovative

design. We argue, that the integration of AI in culture

and design study programs not only needs to empower

students with the skills to engage with AI effectively

but also needs to address issues such as bias, fairness

and copyright. In this paper, the integration potential

of AI into cultural science and design studies is ex-

plored based on two exemplary study programs at a

German University of Applied Sciences. A compre-

hensive analysis of the programs’ class descriptions

revealed initial connections. This was followed by

two expert interviews with lecturers and reviews of

projects that gave further insights. The ﬁndings are

not only pertinent to these speciﬁc programs but also

offer valuable lessons and considerations for broader

applications in culture and design education.

2 STATE OF THE ART

In the evolution of the cultural sector, the integration

of modern technology has been a longstanding prac-

tice. Since the 1970s, Galleries, Libraries, Archives

and Museums (GLAM) have continuously digitised

Schauer, S. and Simbeck, K.

AI Literacy for Cultural and Design Studies.

DOI: 10.5220/0012609200003693

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 16th International Conference on Computer Supported Education (CSEDU 2024) - Volume 2, pages 39-50

ISBN: 978-989-758-697-2; ISSN: 2184-5026

their collections (Terras et al., 2021). Digitisation

strategies play a major role in keeping and distributing

the values of arts and heritage by increasing access to

broader audiences, supporting preservation and pro-

tection, collections development, raising the proﬁle of

collections and institutions, and supporting research,

education and engagement (Terras et al., 2021).

Technology use has also increased in the creative

industries. Abbasi et al. (2017) state that this has

led to the emergence of several trends. One trend,

among others, is new forms of artistic expression and

novel art genres that have evolved. They further say

that fresh perspectives on creativity are seen in muse-

ums, theatres and galleries, along with innovative ma-

terials, processes and tools for creative practices and

different approaches to marketing and selling creative

products and services.

2.1 Digitalisation and AI in Culture and

Design

Different ﬁelds of AI are applied in culture and de-

sign. Natural language processing (NLP) is an early

domain in machine learning. It can include dif-

ferent applications like machine translation, speech

recognition and text processing which can be used

for historical text translation and transcription (Raina

et al., 2022; Sporleder, 2010). Further, AI algo-

rithms could be applied for improved accuracy in im-

age recognition to detect cultural relics (Wang and Li,

2022). While Galleries, Libraries, Archives and Mu-

seums are increasingly digitising their extensive col-

lections of cultural heritage, enhancing accessibility

through the support of AI technology should be the

aim (Sporleder, 2010; Caramiaux, 2020).

AI has impacted the creative and cultural sectors

in recent years immensely. In reports commissioned

by the European Commission, the European Parlia-

ment and a European association the impact of AI in

the creative and cultural sectors has been investigated

(Amato et al., 2019). Caramaiaux identiﬁes the po-

tential of AI as ”a tool for information management

and cataloguing digiti[s]ed cultural artefacts”. He de-

tails two examples where AI has already been used

for the digitalisation and management of large data

sets. Further, AI gives opportunities for interactively

engaging the public with artefacts in cultural institu-

tions through the use of chatbots. Another opportu-

nity regards AI’s ability to personalise the visitors’

experience, which is motivated by the goal of predict-

ing an exhibition’s popularity and therefore helps in

saving money and managing resources. This was al-

ready successfully executed in a project by the UK’s

National Gallery, where the future number of visitors

is prognosticated based on an exhibition’s character-

istics (Kulesz, 2020). Lastly, AI holds the potential to

”generate content and reﬂect on existing collections

of data” (Caramiaux, 2023). Several projects have il-

lustrated this potential. The Museum of Modern Art

in New York commissioned the artist Reﬁk Anadol

to train a generative AI model based on a data set of

180,000 art pieces for the museum. The result was a

dynamic abstract artwork that has an impact not solely

from its visuals but also its demonstration of the po-

tentials residing in machine learning models (Zylin-

ska, 2023; Caramiaux, 2023).

As digitalisation is taking place in all areas of so-

ciety, including cultural institutions that embrace dig-

ital transformations and integrate AI technologies into

their practices, challenges in human-AI interaction

are arising. With education being affected as well, ad-

dressing these challenges is becoming a responsibility

of education stakeholders (Schumann et al., 2020).

2.2 AI Literacy

The term AI literacy has been deﬁned by Long and

Magerko (2020) ”as a set of competencies that en-

ables individuals to evaluate AI technologies criti-

cally; communicate and collaborate effectively with

AI; and use AI as a tool online, at home, and in the

workplace.” They state AI literacy exists within a web

of interconnected literacies, which are all beneﬁcial

for successful interaction with AI. Digital literacy, the

ability to navigate computer interfaces; computational

literacy, involving programming skills; and scientiﬁc

literacy, particularly in machine learning, build the

foundation for users but are not obligatory. How-

ever, data literacy, ”the ability to read, work with,

analy[s]e, and argue with data as part of a broader

process of inquiry into the world” (D’Ignazio, 2017),

is an essential part and shares similar competencies

with AI literacy (Long and Magerko, 2020).

Ng et al. (2021) underline the importance of fun-

damental AI skills for the general public and for-

mulate three main aspects of creating AI literacy:

knowing and understanding AI, using and applying

AI, evaluating and creating AI. In addition, they em-

phasised human-centred considerations for promoting

awareness and educating individuals to become so-

cially responsible and ethical users of AI. This educa-

tional focus goes beyond merely enhancing students’

AI skills. It creates key values like ”inclusiveness,

fairness, accountability, transparency, and ethics” (Ng

et al., 2021). By prioritising these principles, the aim

is to foster technical proﬁciency and demonstrate an

understanding of the societal implications and ethical

dimensions associated with AI technologies.

CSEDU 2024 - 16th International Conference on Computer Supported Education

Several approaches have been published to mea-

sure AI competencies. In the study conducted by

Laupichler et al. (2023), AI literacy was categorised

into the areas of ”Technical Understanding”, ”Criti-

cal Appraisal” and ”Practical Application”. Compe-

tencies covering the data-driven nature of AI and the-

oretical understanding of AI are collected as ”Tech-

nical Understanding”, whereas ”Critical Appraisal”

covers skills for ethically reﬂecting, evaluating the re-

sults of AI applications and managing legal issues.

Other competencies relating to AI application exam-

ples or recognising ”if a problem in [one’s] ﬁeld can

and should be solved with artiﬁcial intelligence meth-

ods” (Laupichler et al., 2023) are included in ”Prac-

tical Application”. They developed a ”scale for the

assessment of non-experts’ AI literacy” (SNAIL), tar-

geted at people with no prior AI education, the em-

phasis was on engaging or cooperating with AI rather

than creating or developing it.

Further research by Wang et al. (2023) cate-

gorised AI literacy into awareness, usage, evaluation

and ethics. In this study, ”awareness” refers to users’

capability to recognise and understand AI technol-

ogy when engaging with applications. ”Usage” on

the other hand stands for the necessary skills to effec-

tively apply and utilise AI technology for tasks. Their

third core construct ”evaluation” involves the ability

to analyse, choose, and critically assess AI applica-

tions and their outcomes. Lastly, Wang et al. (2023)

deﬁne ”ethics” as the capability of being aware of the

responsibilities and risks linked with AI technology.

The categories proposed by Williams (2023) are

similar to the categories suggested by Laupichler et

al. (2023). The category ”concepts” could be com-

pared to ”technical understanding” and includes AI

background knowledge and interdisciplinary knowl-

edge. In ”practices”, similar to ”practical applica-

tion”, Williams (2023) mentions skills such as con-

structing, analysing and communicating about AI.

The third part, ”perspectives”, contains (critical) digi-

tal literacy, identity and social awareness and is com-

parable to ”critical appraisal” (Williams, 2023).

A different approach to AI literacy was taken by

Faruqe et al. (2021) who proposed different roles re-

lated to AI which require different competencies. The

ﬁrst role is described as ”consumer”, ”who uses the

output of AI to improve their work or life” (Faruqe

et al., 2021). A second role consists of co-workers,

who possess a foundational understanding of AI sys-

tems and employ the results in their professional

tasks. The collaborator works with multiple AI sys-

tems to enhance their performance. The fourth role

is creators who engage in the developing and testing

of novel AI systems and their model (Faruqe et al.,

2021).

In the subsequent chapter, the methodology will

explore AI competencies speciﬁcally for culture and

design.

3 METHODOLOGY

The process of identifying relevant AI competencies

for students in cultural and design studies was di-

vided into three parts: a comprehensive analysis of

two study programs, expert interviews with respective

lecturers and an analysis of existing student projects.

The analysis was conducted on two speciﬁc study pro-

grams offered by a German University of Applied Sci-

ences. These programs were chosen as they contain

theoretical and practical cultural and design studies,

as well as their hold high potential for integrating AI

technology.

3.1 Curricula Analysis

For the conduction of the curricula analysis, the over-

all structure of the two study programs is explained

and selected courses are highlighted.

3.1.1 Museology

The ﬁrst program to be analysed is the museology

course

, which provides students with insights into

contemporary museum practices. It equips them with

the skills and knowledge needed to engage with the

dynamic demands of the modern museum sector and

was chosen because of its breadth of classes and

application-oriented focus.

In a few fundamental classes, students learn topics

such as inventory management, documentation, and

essential museum management tools. Additionally,

they gain a foundational understanding of art and cul-

tural history, which serves as a valuable contextual

backdrop for their work.

Following, students apply their acquired skills in

real-world museum settings, gaining hands-on experi-

ence. Their education continues by exploring various

types of collections and academic disciplines. More-

over, the program emphasises the importance of com-

munication strategies in museum education, innova-

tive exhibition design, public relations, and visitor re-

search.

In specialisation classes, students are required to

opt for three courses from a selection that includes

Cultural Economy, Publications, Digital Media, Mod-

ern Materials, Digitalisation or Provenance Research.

https://museologie.htw-berlin.de/international-en/

AI Literacy for Cultural and Design Studies

This specialisation semester is succeeded by the com-

pletion of the Bachelor’s thesis.

The analysis of the classes and their descrip-

tion revealed possible connections with AI in several

courses. In ”Museum Documentation”, students learn

the theoretical basics of relational database models

and standardised data entry formats. Combining

these practices with AI fundamentals, such as ma-

chine learning and data analysis, could enhance ef-

ﬁciency and accuracy when working with large vol-

umes of data for archival documentation (Colavizza

et al., 2021). This could include making use of au-

tomated data entries and metadata generation as well

as algorithms for the recognition of entities and key-

words but also navigating potential bias and fair rep-

resentation (Lowagie, 2023).

Furthermore, ”Curating of Exhibitions” teaches

methods for the concept, planning, organisation and

production of exhibitions, as well as fostering a crit-

ical reﬂection on these processes. Using AI tools for

exhibition planning or curatorial research can help in

the creative process but has to be critically assessed at

the same time (Jo et al., 2022; Zhao et al., 2020).

In addition, ”Visitor Research and Service” aims

to provide students with knowledge on the structural

organisation of museums as well as methods for em-

pirical visitor research. Combining these practices

with AI could help in analysing visitors’ behaviour

or predicting future trends (Rani et al., 2023). Fur-

ther, the need for giving visitors personalised exhi-

bition experiences and developing recommender sys-

tems through AI has been recognised by museums

and should be catered to (Bordoni et al., 2013)

In ”Strategies for Digitalisation” students are fa-

miliarised with the techniques, methods, and stan-

dards for digitising and making collection invento-

ries available online and get acquainted with digi-

talisation and online presentation strategies, includ-

ing ﬁnding project partners and fundraising. Students

should furthermore develop skills for the ethical han-

dling of digital cultural pieces to correctly deal with

growing numbers of digitalised cultural collections

(Groumpos, 2022; Pansoni et al., 2023).

Moreover, in the module ”Inventory Design” stu-

dents learn the fundamentals and methods of inven-

torying and documenting museum collections. They

can identify important materials and analyse the artis-

tic and craft techniques of museum objects and are

capable of handling museum objects professionally,

as well as capturing, describing and accessing them.

Learning correct preservation techniques in combina-

tion with AI technologies could therefore be highly

useful (Marchello et al., 2023).

3.1.2 Communication Design

The second study program closely investigated is

communication design

. This program prepares stu-

dents for both the practical and technical requirements

of a professional career in the media sector. Its goal

is to cultivate creative, collaborative and independent

designers with a systemic mindset and a sense of re-

sponsibility.

The basics of the study course cover design es-

sentials, experimental methods and the historical as-

pects of crafts such as print typography, illustration

and photography.

Once students have acquired foundational knowl-

edge, design workshops are combined with lectures,

methodological training and technological education.

Within this context, students choose a specialisation

to focus on. International experts contribute different

perspectives and insights into the latest industry de-

velopments and practices through workshops and lec-

tures. Interdisciplinary projects, including collabora-

tions with other university courses, form an integral

part of the curriculum.

In two classes on ”Technologies”, students are

introduced to digital tools and software to gain an

understanding of programming processes and their

use cases in interdisciplinary projects. Focusing also

on the transfer of analogue media to digital formats,

especially in terms of publishing methods in multi-

media ﬁelds, such as e-books, ﬁlm or motion design.

Teaching students a basic skill set for digitalisation

and transcription through NLP technology could en-

hance and optimise their work (Piotrowski, 2012).

Multiple classes on ”Design Foundations” im-

part knowledge on design processes and terminology

across various design domains, emphasising applying

methodological basics to visualise and create prod-

ucts. The competencies span from three-dimensional

and plastic design, simple shape, colour and material

theories, to typography and assessment of font effects

and their application in design projects. These de-

sign processes could be combined with generative AI

tools to generate ideas and concepts while critically

assessing usability as well as conforming to accessi-

bility and inclusivity guidelines (Tholander and Jons-

son, 2023).

In ”Design Laws and Ethics” students are edu-

cated on issues related to the right to one’s own words,

images and ideas, involving general legal concepts,

exploring aspects of protective and usage rights, ad-

dressing contract design and considering ethical con-

siderations in communication practices. The focus is

on developing awareness of these challenges and lay-

https://kd.htw-berlin.de/international-en/

CSEDU 2024 - 16th International Conference on Computer Supported Education

ing the groundwork for effective communication with

experts in the ﬁeld. Including education on current

laws and regulations on AI and AI-generated results

will become more unavoidable (Fui-Hoon Nah et al.,

2023).

Furthermore, ”Presentation and Documentation of

Designs” teaches competencies in domain-speciﬁc

skills for presenting design products and processes,

both verbally and visually, stemming from one’s de-

sign work. Skills and knowledge in two- and three-

dimensional representation and documentation of de-

sign processes and outcomes are taught to the stu-

dents and could possibly be combined with AI tools

(Tholander and Jonsson, 2023).

Additionally, in ”Materials and Sustainability”

students learn to recognise and understand the eco-

logical aspects of product development. The differ-

ent consequences of technological advancements are

illustrated and alternative technologies, renewable re-

sources, sustainable materials and products are for-

mulated. Understanding and critically assessing the

environmental impacts of technology usage including

AI should be highlighted (Van Wynsberghe, 2021).

3.2 Expert Interviews

As a second step expert interviews were conducted

with lecturers from the two study programs to gather

further insight into the teachings at the university.

3.2.1 Museology

An expert interview with a museology professor gave

insights into the teachings. The professor has been

an advocate for implementing digitalisation strategies

as part of the curriculum for years and emphasised

the critical need for students to adapt to evolving dig-

ital standards. One of their classes on ”Documen-

tation” focuses on the safekeeping of archival docu-

ments and artefacts. Documenting these cultural ob-

jects often requires speciﬁc formatting for the upload

to public databases. However, it was expressed that

non-standardised data formats are still in use, or var-

ious databases that still obtain old data, hindering the

seamless exchange of information within the cultural

heritage community. Teaching students how to use AI

to automatically generate data in the correct format or

clean out outdated data could therefore be a great ad-

dition to their curriculum, according to the professor.

3.2.2 Communication Design

In an expert interview, a professor from communi-

cation design gave an overview of their courses and

offered insights into the content and structure of de-

sign classes. From their experience, many students

already have signiﬁcant knowledge of AI tools and

regularly apply them in their project work. What they

usually lack are a deeper understanding of AI func-

tionality and critical reﬂection on their AI usage and

AI-produced results. According to the professor, they

are familiar with a variety of AI tools and understand

how to use them but have no deeper knowledge of

how AI tools produce their results and what inaccura-

cies or errors to look out for. The professor advocated

for an educational approach that goes beyond mere

tool proﬁciency, encouraging students to explore the

theoretical foundations of AI, the ethical implications

surrounding its implementation and its potential im-

pact on the future of design. Increasing their general

knowledge of AI and therefore developing an AI lit-

eracy should therefore be of high priority.

During a second interview with a design lecturer,

the ethical reservations held by students regarding the

use of AI-generated art were revealed. According to

the professor, many students have concerns related

to the origin and ownership of artworks produced

through AI algorithms, as well as the potential for

replication or infringement on existing artistic works.

Students express a genuine interest in understanding

the ethical implications of incorporating AI-generated

elements into their projects. By creating a dialogue

on ethical practices, students would be able to make

informed decisions about incorporating AI into their

creative processes while respecting the principles of

artistic integrity and legal considerations.

3.3 Example Projects

As a third part of the methodology, a few example

projects from the study courses were investigated.

Some have already been created with AI or have a

thematic focus on AI, while others show great poten-

tial for AI integration.

3.3.1 Museology

In the ﬁrst project, 52 fabric pattern books were dig-

italised using two professional scanners. Metadata

was allocated to the fabric data sets using the Adlib

Information Systems software. An initial database

has been completed and is now continuously opti-

mised and extended (Haffner and Hornscheidt, 2015).

Adlib allows for LIDO exports, the standard format

required for online data portals like DBB

, the Ger-

man digital library with an array of cultural and sci-

https://www.deutsche-digitale-bibliothek.de/?lang=en

AI Literacy for Cultural and Design Studies

entiﬁc resources, and Europeana

, a European digital

platform to access millions of cultural heritage items

from museums, galleries, libraries and archives. Eu-

ropeana has been instrumental in advancing the digi-

tisation of cultural heritage across Europe (Macr

ı and

Cristofaro, 2021).

Figure 1: Digitalisation of Fabric Books (Haffner and Horn-

scheidt, 2015).

The fabric pattern books themselves, as well as

their digitised data, hold great potential for interdis-

ciplinary projects that involve AI technology. An AI-

driven image recognition algorithm could for exam-

ple be used for pattern comparison across data sets

and suggestions based on visual similarities. Fun-

damental knowledge in machine learning would al-

low students to classify the data and implement an

AI-powered search functionality based on patterns,

colours, material or keywords. Deng et al. (2023)

used AI technology for fabric property analysis and

proved AI’s potential for pattern recognition and eth-

nic fashion design.

In the second project, the cultural heritage in

public spaces in Berlin was digitalised, includ-

ing sculptural monuments, fountains and ﬁne arts.

All elements are documented in a database acces-

sible through a website, showcasing the location

and further information about the cultural pieces.

Bildhauerei-in-Berlin (BiB)

contains around 2,500

entries for a variety of cultural heritage. Students

from museology heavily contributed to the data col-

lection process for objects from the districts K

openick

and Lichtenberg.

https://www.europeana.eu/en

https://bildhauerei-in-berlin.de/was-ist-bib/

Figure 2: Map of Cultural Heritage in Berlin (Screenshot

from https://bildhauerei-in-berlin.de/karte/).

The data entries contain various arrays of infor-

mation and can be searched regarding epoch, mate-

rial, category, technique, condition, completeness and

location. A selection of ﬁve objects have been virtu-

alised as 3D models.

Figure 3: 3D Visualisation of Object (Screenshot from

https://bildhauerei-in-berlin.de/bildwerk/knabe-mit-pony-

6286/).

The BiB database could serve as a practical ex-

ample for teaching AI concepts such as classiﬁcation

algorithms or basics of data analysis while also teach-

ing the importance of data literacy and ethical data

usage. Generative AI tools could also be tested for

virtual reconstruction of missing or damaged parts.

3.3.2 Communication Design

During the Ars Electronica festival from 7

to 11

September 2022 in Linz, Austria, a variety of projects

for art, technology and culture were exhibited. Stu-

dents from the communication design study course

presented seven of their projects on ”Post-Intelligent

Artiﬁcial Humanism”

at the exhibition. The projects

focused on the impact of AI on humanity and the con-

cept of ”Planet B” while raising questions about inter-

actions with a new form of intelligence and the con-

sequences, both positive and negative, of integrating

AI into our lives and society.

https://ars.electronica.art/planetb/de/artiﬁcial-

humanism/

CSEDU 2024 - 16th International Conference on Computer Supported Education

Figure 4: Project ”Pattern” at Ars Electronica (B

urger et al.,

2022).

One exemplary project was ”Pattern” by Maria

urger, Lea Gleisberg and Jana Staltmayer. The

project utilises a neural network to interpret and

recognise the meaning of doodles and is centred on a

vast database comprised of globally contributed doo-

dles. It further involves extracting scribbles from

the database and employing a robotic drawing ma-

chine to overlay hundreds of them. The key question

is whether the resultant image constitutes an anthro-

pological study or an artwork (B

urger et al., 2022).

Demir et al. (2021) took a similar approach and

trained a neural network model to identify and cat-

egorise design principles in contemporary buildings.

These projects showcased the interdisciplinarity of AI

and design, speciﬁcally having an understanding of

neural network functionality as well as handling ex-

tensive data sets and being aware of the data being

utilised.

Figure 5: Project ”Bot-I-Celli” at Ars Electronica (Ball

et al., 2022).

Further, the work ”Bot-I-Celli” by

Anne Ball,

Anna Brauwers and Anastasia Scherf explores the in-

tersection of AI and creativity and questions whether

AI can be considered creative. It delves into the future

of art in a scenario where AI overtakes tasks tradition-

ally performed by humans. The experience involves

personalised portraits created by a robot, resembling

caricatures drawn by street artists. The central focus

point is the perceptual shift when the role of the artist

transitions from a human to a robot (Ball et al., 2022).

The project displayed the human-AI co-creative pro-

cess and used algorithms for creativity. Students not

only needed to understand how AI algorithms work

but also develop their own to generate creative out-

put.

4 AI COMPETENCIES

The following chapter deﬁnes speciﬁc competencies

identiﬁed through the interviews and analyses and ex-

plains how these can be integrated. The three major

ﬁelds of competencies that have been deﬁned cover

technology, ethics and application and can, there-

fore, be best assessed through the categories made

by Laupichler et al. (2023): technical understanding,

critical appraisal and practical application.

4.1 Technical Understanding

AI competencies for both cultural and design studies

place a strong emphasis on fostering the technical un-

derstanding of machine learning, AI tool proﬁciency

and data analysis. These skills are essential for navi-

gating the complexities of AI applications. Technical

competencies for both ﬁelds should therefore be:

• AI Tool Proﬁciency: Acquiring an in-depth under-

standing of AI tools

• Machine Learning Fundamentals: Knowledge of

ML concepts for developing models to predict ob-

ject labels and features

• Data Analysis: AI-driven analysis of databases re-

garding object features

For cultural studies, developing proﬁciency in

data analysis is crucial for extracting meaningful in-

formation from data sets, as well as automating the

process of formatting and cleaning data and recognis-

ing errors for effective database management. This in-

cludes extracting relevant information, ensuring data

quality and preparing data sets for model training.

The focus is not only on technical proﬁciency but also

on the students developing a deeper understanding

of machine learning functionalities, enabling them to

use this technology for tasks like archival documen-

tation, object classiﬁcation and predictive analytics.

The competencies speciﬁcally for cultural sciences

could be:

• Automated Data Entry: Receiving suggestions for

standardised formats

• Automated Metadata Generation: Automatically

generate metadata for data entries

• Error Recognition: Ability to identify inaccura-

cies and errors in AI-produced results

AI Literacy for Cultural and Design Studies

• Named Entity Recognition: NLP integration to

analyse and understand entities and keywords

Required AI competencies in design studies focus

especially on general tool proﬁciency and a strong un-

derstanding of the underlying technology. This allows

for informed decisions and critical assessment of AI-

generated results. In that way, design students can

use AI as a powerful tool to generate creative output

and develop their own algorithmic tools, while also

acquiring the knowledge to navigate potential risks

(Holmquist, 2017). The suggested technical compe-

tencies for design are as follows:

• Algorithmic Knowledge: Understanding funda-

mental principles of AI tools to comprehend how

algorithms generate results

• Neural Network Understanding: Knowledge of

how neural networks operate, especially in the

context of image interpretation and recognition

• Algorithmic Creativity: Writing AI algorithms for

generating creative output

Figure 6: Technical Understanding Competencies for Cul-

ture and Design.

4.2 Critical Appraisal

Proﬁciency in critically evaluating and reﬂecting on

AI usage is an essential part of AI literacy for cultural

scientists and designers. This involves creating a deep

understanding of the legal landscape surrounding AI-

generated content, copyright regulations and ethical

considerations related to the ownership and attribu-

tion of such works (Hayes, 2023). An emphasis is

also on ensuring fair and unbiased data use in AI ap-

plications. In other words, developing a strong form

of data literacy is important. It involves having an

understanding of the data sources, their quality and

potential biases that may inﬂuence AI outcomes. The

following competencies are proposed for both ﬁelds:

• Copyright Knowledge: Familiarising with current

laws on copyright of AI-generated content

• Data Literacy: Knowing what and by whom data

is used for model training

• Ethical Data Usage: Use of fair and unbiased data

for analyses

AI literacy for culture must contain skills to crit-

ically evaluate the legal and ethical implications of

employing AI in cultural projects, especially for the

handling and preservation of digitalised cultural ob-

jects (Groumpos, 2022). Further, AI can be used as

a tool for curatorial research but its output should al-

ways be judged evaluatively. Potential competencies

for the culture in this category are:

• Curatorial Research: Assistance in researching

and developing exhibitions with AI tools

• Digital Cultural Heritage Preservation: Expertise

in using AI for preservation with respect to au-

thenticity, diversity and integrity of cultural arte-

facts

• Ethical Handling of Digital Cultural Objects: Re-

sponsible managing and interacting with cultural

objects through AI

Using generative AI tools for creative design pro-

cesses can support designers in creating more acces-

sible and inclusive concepts and adhering to guide-

lines more easily. This involves an awareness of po-

tential biases and the proactive inclusion of features

that cater to diverse users. Utilising AI to evaluate de-

sign concepts from the perspective of end-users can

ensure alignment with user needs, preferences and

expectations, fostering a strong user-centred design

process (Tholander and Jonsson, 2023). Addition-

ally, acknowledging the environmental impact of AI

is crucial for designers. It not only involves assessing

the ecological footprint of used technologies but also

considers factors such as energy consumption and re-

source usage, helping them to make informed deci-

sions and contributing to more sustainable and re-

sponsible design practices (Van Wynsberghe, 2021).

These competencies are advised for design studies:

• Inclusive Design Considerations: Consideration

of inclusivity in AI-generated design

• User-Centred Evaluation: Assessment of design

regarding usability

• Environmental Impact of AI: Acknowledging eco-

logical consequences of AI technology

CSEDU 2024 - 16th International Conference on Computer Supported Education

Figure 7: Critical Appraisal Competencies for Culture and

Design.

4.3 Practical Application

Competencies for practically applying AI in cultural

and design projects include proﬁciency in digitalisa-

tion tools which help both cultural scientists and de-

signers transition from analogue to digital formats,

preserving cultural artefacts and media. This also in-

cludes using NLP for transcription purposes to au-

tomatically convert spoken or written texts into dig-

ital documents, enhancing their accessibility. The co-

creation between humans and AI supports both dis-

ciplines in developing a collaboration where human

creativity utilises AI capabilities to produce innova-

tive results (Wingstr

om et al., 2023; Tholander and

Jonsson, 2023). Both study ﬁelds could, therefore,

beneﬁt from these practical competencies:

• Digitalisation Tool Proﬁciency: AI-based digital-

isation for analogue to digital formatting

• Human-AI Co-Creation: Creating synergies be-

tween humans and AI for artistic purposes

• NLP Transcription: NLP for automated digitali-

sation of spoken or written texts

Speciﬁcally, in cultural use cases, this could in-

volve generative AI tools that are used for exhibi-

tion design (Oksanen et al., 2023), virtual reconstruc-

tion of cultural heritage objects (Moral-Andr

es et al.,

2023) or an analysis of visitor behaviour, including

predictions of future visitor trends (Rani et al., 2023).

With a wide skill set and in-depth knowledge of AI

technologies, creative and innovative cultural projects

can be conducted and the everyday work of cultural

scientists supported. For cultural studies, the follow-

ing competencies are put forward:

• Exhibition Design: Spatial analysis for layout and

visitor ﬂow optimisation

• Virtual Reconstruction: Use of generative AI for

virtual reconstruction of missing or damaged cul-

tural objects

• Visitor Behaviour Analysis: Understanding visitor

movement in museums and other cultural institu-

tions

• Predictive Visitor Trends: Forecasting visitor

trends and anticipating peak visiting times for op-

timised staff management

Practically applying AI in design processes fo-

cuses especially on fostering creative collaboration

between AI systems and designers. A range of AI

tools can be used for design purposes including idea

generation or content creation but also testing prod-

ucts for accessibility and inclusivity. AI can function

as a versatile technology, offering insights, generating

innovative concepts and streamlining repetitive tasks

(Fatima, 2023; Caramiaux, 2020). In design, these

practical competencies could be advantageous:

• Accessibility and Inclusivity Testing: Testing for

accessibility and inclusivity with AI

• Content Design: Use of AI for creation of design

elements

• Idea Generation: AI tools for supporting the cre-

ative process

Figure 8: Practical Application Competencies for Culture

and Design.

5 CONCLUSION

In this paper, we have applied the AI literacy frame-

work by Laupichler et al. (2023) to culture and design

studies. Two study programs in museology and com-

munication design were used as examples. Through

curricula analysis, expert interviews and project re-

views we identiﬁed AI competencies important to

those ﬁelds.

AI Literacy for Cultural and Design Studies

The competencies developed through museology

studies, including inventory management, documen-

tation, exhibition curation and visitor research, pro-

vide a solid foundation for integrating AI technolo-

gies into the curriculum. Communication design stu-

dents can use AI to iterate collaboratively through de-

sign tasks, concepts and ideas.

Technical competencies focused in both disci-

plines on general tool proﬁciency, machine learning

fundamentals and data analysis understanding. For

cultural specialists, this means in particular increased

efﬁciency when working with databases, while de-

signers are supported in their creative process.

Critically reﬂecting on AI use is a key component

of AI literacy and knowledge on copyright issues, fos-

tering data literacy and usage of ethical and fair data

are the main competencies in culture and design. For

both studies speciﬁcally, this involves the sensitive

and responsible handling of cultural objects and fo-

cusing on accessible and inclusive designs.

AI competencies can be practically applied in

many use cases, however, a general digitalisation tool

proﬁciency, the sense of human-AI co-creation and

NLP transcription were found to be essential for both

ﬁelds. In the cultural sector, practical AI use cases

could include analysis and prediction of visitors or

exhibition design and curation. AI in design, on the

other hand, is applied for generating design ideas and

concepts, as well as for producing content.

The concepts presented in this paper will provide

a foundation for educational programs and curricula

development. While this study was limited to two ex-

emplary study programs, the methodology can also be

used in future more extensive research at other univer-

sities as well as different disciplines.

ACKNOWLEDGEMENTS

This paper portrays the work carried out in the con-

text of the KIWI project (16DHBKI071) that is gen-

erously funded by the Federal Ministry of Education

and Research (BMBF).

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