AI Literacy Framework and Strategies for Implementation in Developing Nations

Abstract

Artificial Intelligence (AI) k is a transformative force in the 21st cen
tury, offering significant potential to revolutionize various sectors,
including education. However, the disparity in AI literacy between
developed and developing nations poses significant challenges to
achieving equitable global progress. This review paper aims to ad
dress these challenges by proposing a comprehensive AI literacy
framework tailored for developing nations and outlining strategic
implementation plans. The paper begins by defining AI literacy and
emphasizing its critical importance for individuals and societies,
particularly in the context of developing nations. It provides an
in-depth analysis of the current state of AI literacy in these regions,
identifying key barriers such as limited infrastructure, insufficient
educational resources, and policy gaps. A detailed AI literacy frame
work is proposed, focusing on essential competencies and skills,
curriculum integration, and the pivotal role of educator training.
Strategies for effective implementation are explored, including pol
icy recommendations for governments and educational institutions,
the role of public-private partnerships, and the utilization of online
educational platforms to enhance accessibility and inclusivity. The
review highlights successful case studies from various developing
nations, extracting best practices and lessons that can be adapted
and replicated. Methods for assessing the impact of AI literacy pro
grams are discussed, emphasizing the long-term benefits for both
individuals and the broader society. Finally, the paper considers
future directions and innovations in AI literacy, suggesting ways to
improve and adapt the framework to meet evolving needs continu
ously. By thoroughly reviewing the existing literature and offering
actionable strategies, this paper aims to contribute to the global
effort to promote AI literacy and ensure that developing nations
are not left behind in the AI-driven future.

Full text

AI Literacy Framework and Strategies for Implementation in
Developing Nations
Krishna Chaitanya Rao Kathala
University of Massachusetts Amherst
Amherst, Massachusetts, USA
kkathala@umass.edu
Shashank Palakurthi
Christian Brothers University
Memphis, Tennesse, USA
palakurthi.shashank@gmail.com
Abstract
Articial Intelligence (AI) k is a transformative force in the 21st cen-
tury, oering signicant potential to revolutionize various sectors,
including education. However, the disparity in AI literacy between
developed and developing nations poses signicant challenges to
achieving equitable global progress. This review paper aims to ad-
dress these challenges by proposing a comprehensive AI literacy
framework tailored for developing nations and outlining strategic
implementation plans. The paper begins by dening AI literacy and
emphasizing its critical importance for individuals and societies,
particularly in the context of developing nations. It provides an
in-depth analysis of the current state of AI literacy in these regions,
identifying key barriers such as limited infrastructure, insucient
educational resources, and policy gaps. A detailed AI literacy frame-
work is proposed, focusing on essential competencies and skills,
curriculum integration, and the pivotal role of educator training.
Strategies for eective implementation are explored, including pol-
icy recommendations for governments and educational institutions,
the role of public-private partnerships, and the utilization of online
educational platforms to enhance accessibility and inclusivity. The
review highlights successful case studies from various developing
nations, extracting best practices and lessons that can be adapted
and replicated. Methods for assessing the impact of AI literacy pro-
grams are discussed, emphasizing the long-term benets for both
individuals and the broader society. Finally, the paper considers
future directions and innovations in AI literacy, suggesting ways to
improve and adapt the framework to meet evolving needs continu-
ously. By thoroughly reviewing the existing literature and oering
actionable strategies, this paper aims to contribute to the global
eort to promote AI literacy and ensure that developing nations
are not left behind in the AI-driven future.
CCS Concepts
•Social and professional topics
→
Professional topics;Com-
puting education;Computing literacy;
Keywords
Articial Intelligence (AI), STEM Education, Equity, AI Frameworks
This work is licensed under a Creative Commons Attribution International
4.0 License.
ICETC 2024, September 18–21, 2024, Porto, Portugal
©2024 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-1781-9/24/09
https://doi.org/10.1145/3702163.3702449
ACM Reference Format:
Krishna Chaitanya Rao Kathala and Shashank Palakurthi. 2024. AI Literacy
Framework and Strategies for Implementation in Developing Nations. In
2024 the 16th International Conference on Education Technology and Comput-
ers (ICETC) (ICETC 2024), September 18–21, 2024, Porto, Portugal. ACM, New
York, NY, USA, 5 pages. https://doi.org/10.1145/3702163.3702449
1 Introduction
Articial Intelligence (AI) has emerged as a transformative force
in the 21st century, profoundly impacting various sectors of soci-
ety. AI, broadly dened as the development of computer systems
capable of performing tasks that typically require human intelli-
gence, is reshaping how we live, work, and interact with the world
around us as per Russell & Norvig, 2021 [1]. The potential of AI to
revolutionize diverse sectors, including education, economics, and
healthcare, is immense and far-reaching. In education, AI-powered
adaptive learning systems can tailor instruction to individual stu-
dent needs, potentially bridging achievement gaps and enhancing
learning outcomes as per Holmes et al., 2019 [2]. The healthcare
sector is witnessing AI applications ranging from early disease
detection to drug discovery, promising more ecient and personal-
ized medical care as per Topol, 2019 [3]. Economically, AI’s impact
is equally signicant, with estimates suggesting it could add up to
$15.7 trillion to the global economy by 2030 as per PwC, 2020 [4].
Given AI’s pervasive inuence, the importance of AI literacy
cannot be overstated. AI literacy encompasses the knowledge, skills,
and attitudes necessary to understand, critically evaluate, and en-
gage with AI technologies as per Long & Magerko, 2020 [5]. It goes
beyond mere technical prociency, encompassing ethical consid-
erations and societal implications. As AI systems become more
integrated into our daily lives, AI literacy is becoming as funda-
mental as traditional literacy and numeracy skills.
The relevance of AI literacy in the 21st century is particularly
pronounced in terms of economic and social development. As in-
dustries increasingly adopt AI technologies, individuals with AI
literacy are better positioned to participate in and contribute to
the evolving job market. Moreover, AI literacy empowers citizens
to engage in informed discussions about the ethical and societal
implications of AI, ensuring that the development and deployment
of these technologies align with human values and societal needs
as per Floridi et al., 2018 [6]. However, the global landscape of AI
literacy is marked by signicant disparities between developed and
developing nations. While countries like the United States, China,
and those in the European Union are making substantial invest-
ments in AI education and research, many developing nations lag
behind due to various structural and systemic challenges as per
Vinuesa et al., 2020 [7]. This disparity raises concerns about the

ICETC 2024, September 18–21, 2024, Porto, Portugal Kathala KK and Palakurthi SP
potential for a widening global digital divide, where developing
nations risk falling further behind in the AI-driven global economy.
The implications of this AI literacy gap are far-reaching. As AI
technologies increasingly drive economic growth and innovation,
nations with lower levels of AI literacy may nd themselves at a
competitive disadvantage in the global marketplace as per Makri-
dakis, 2017 [8]. This could exacerbate existing economic inequalities
and limit opportunities for social mobility in developing nations.
Furthermore, the lack of AI literacy could hinder these countries’
ability to harness AI to address pressing local challenges, such as
improving healthcare delivery or enhancing agricultural productiv-
ity.
Developing nations face a multitude of challenges in achieving
widespread AI literacy. Limited infrastructure and technological
resources often restrict access to the tools and platforms necessary
for AI education. Many of these countries struggle with unreliable
internet connectivity and a shortage of computing devices, making
it dicult to implement comprehensive AI education programs as
per Nye, 2014 [9]. The lack of access to quality education and train-
ing programs further compounds the problem. Many educational
institutions in developing nations lack the expertise and resources
to oer AI-related courses. There is often a shortage of qualied
instructors who can eectively teach AI concepts and applications.
Additionally, existing curricula may not adequately address AI lit-
eracy, focusing instead on more traditional subjects as per Goksel
& Bozkurt, 2019 [10].
Insucient policy support and funding present another signif-
icant hurdle. Many developing nations have yet to prioritize AI
education in their national policies, resulting in limited allocation
of resources for AI literacy initiatives. The absence of a coherent
national strategy for AI development and education can lead to
fragmented and ineective eorts to promote AI literacy as per
Dutton, 2018 [11]. “An Overview of National AI Strategies and
Policies,” 2021 [36].
Cultural and linguistic barriers also play a role in impeding AI
literacy in developing nations. Much of the available AI educational
content is in English, which can be a signicant barrier in non-
English speaking countries. Moreover, some cultures may have
reservations about adopting new technologies, necessitating tai-
lored approaches that align with local values and contexts as per
Schi, 2021 [12].
Given these unique challenges faced by developing nations, there
is a pressing need for a tailored AI literacy framework that addresses
their specic circumstances. Such a framework must take into ac-
count the resource constraints, cultural contexts, and developmen-
tal priorities of these nations while providing a comprehensive
roadmap for enhancing AI literacy. This paper aims to address
this need by proposing a comprehensive AI literacy framework
specically designed for developing nations and outlining strategic
implementation plans. By doing so, we seek to contribute to the
global eort to promote AI literacy and ensure that developing
nations are not left behind in the AI-driven future.
The objectives of this paper are multifold. First, we aim to provide
a thorough analysis of the current state of AI literacy in develop-
ing nations, identifying key barriers and opportunities. Second,
we propose a detailed AI literacy framework that focuses on es-
sential competencies and skills, curriculum integration, and the
pivotal role of educator training. Third, we explore strategies for
eective implementation, including policy recommendations, the
role of public-private partnerships, and the utilization of online
educational platforms. The paper is structured as follows: We begin
with an in-depth examination of the current state of AI literacy
in developing nations. This is followed by the presentation of our
proposed AI literacy framework, tailored to address the unique
challenges and opportunities in these regions. We then delve into
implementation strategies, explore case studies of successful AI
literacy initiatives, discuss impact assessment methods, and con-
sider future directions in AI literacy. By providing a comprehensive
review of existing literature and oering actionable strategies, this
paper aims to serve as a valuable resource for policymakers, educa-
tors, and stakeholders in developing nations seeking to enhance AI
literacy. It is our hope that this work will contribute to bridging the
global AI literacy gap and empower developing nations to harness
the full potential of AI for their economic and social development.
2 Literature Review
This section provides a comprehensive review of existing litera-
ture related to AI literacy, exploring its various dimensions, global
perspectives, and the current state of AI literacy in developing na-
tions. The review highlights key challenges and opportunities and
examines the role of educational frameworks and teacher training
in promoting AI literacy.
2.1 Overview of AI Literacy
AI literacy has emerged as a crucial competency in our increasingly
digitalized world. As per Long & Magerko, 2020 [5] dene AI literacy
as "the set of competencies that enables individuals to critically
evaluate AI technologies; communicate and collaborate eectively
with AI; and use AI as a tool online, at home, and in the workplace"
(p. 2). This denition encompasses not only technical knowledge
but also ethical considerations and practical skills.
The components of AI literacy typically include:
•Understanding of core AI concepts and techniques
•
Ability to critically evaluate AI systems and their implica-
tions
•
Ethical awareness and reasoning about AI’s societal impact
•Practical skills in using and interacting with AI tools
AI literacy has become particularly important in education and
workforce development. As per Pedró et al., 2019 [13] argue, "AI
literacy is becoming as fundamental as reading, writing and arith-
metic in preparing students for the future" (p. 17). In the workforce,
AI literacy is increasingly seen as a key skill for employability and
career advancement across various sectors as per Marr, 2018 [14].
2.2 Global Perspectives on AI Literacy
Research on AI literacy globally reveals signicant disparities be-
tween developed and developing nations. A study by ss per Gabriel
et al., 2022 [15] found that while developed countries are rapidly
integrating AI education into their curricula, many developing
nations are still struggling with basic digital literacy. Successful
AI literacy initiatives in developed nations often share common
features. For instance, Finland’s Elements of AI course, a free on-
line program aimed at demystifying AI for the general public, has

AI Literacy Framework and Strategies for Implementation in Developing Nations ICETC 2024, September 18–21, 2024, Porto, Portugal
been praised for its accessibility and practical approach (Elementso-
fai.com, 2021). Similarly, the AI4K12 Initiative in the United States
has been eective in developing guidelines for AI education in
primary and secondary schools as per Touretzky et al., 2019 [16].
These successful programs typically incorporate:
•Hands-on, project-based learning
•
Interdisciplinary approaches that connect AI to various sub-
jects
•Focus on ethical implications and societal impacts of AI
•
Partnerships between educational institutions and industry
2.3 Current State of AI Literacy in Developing
Nations
The state of AI literacy in developing nations presents a complex
picture. A comprehensive report by UNESCO, 2023 [17] highlights
that while there is growing awareness of AI’s importance, many
developing countries lack the infrastructure and resources to im-
plement widespread AI education.
Common challenges identied in the literature include:
•
Limited technological infrastructure: Many schools in devel-
oping nations lack reliable internet connectivity and com-
puter resources, hindering the implementation of AI educa-
tion programs as per Nye, 2014 [9].
•
Shortage of qualied educators: There is a signicant short-
age of teachers trained in AI concepts and pedagogies in
developing countries as per Goksel & Bozkurt, 2019 [10].
•
Curriculum gaps: Many education systems in developing na-
tions have yet to integrate AI literacy into their core curricula
as per Pedró et al., 2019 [13].
•
Language and cultural barriers: Much of the available AI
educational content is in English, posing challenges in non-
English speaking countries as per Gökçearslan et al., 2024
[18].
•
Limited policy support: Many developing nations need more
comprehensive policies to promote AI education and literacy
as per Dutton, 2018 [11].
Despite these challenges, there are also opportunities emerging
in developing nations. For instance, mobile technology penetration
in many of these countries oers a potential platform for delivering
AI education as per GSMA, 2020 [19]. Additionally, international
collaborations and open educational resources are providing new
avenues for AI literacy initiatives in resource-constrained environ-
ments as per Bonami et al., 2020 [20]. Some developing nations are
making strides in AI education. For example, Rwanda has partnered
with Carnegie Mellon University to establish a master’s program in
AI, aiming to build local capacity in this eld AI and Robotics, n.d.,
[33] Similarly, India’s National Education Policy 2020 emphasizes
the integration of AI education across all levels of schooling as per
the Ministry of Education, Government of India, 2020 [35].
In conclusion, while the current state of AI literacy in developing
nations faces signicant challenges, there are also promising ini-
tiatives and opportunities emerging. Addressing these challenges
will require concerted eorts from policymakers, educators, and
international partners to develop tailored, culturally relevant AI
literacy programs that can bridge the global AI literacy divide.
2.4 Educational Frameworks and Curriculum
Development
The development of educational frameworks for AI literacy is an
evolving eld, with various approaches emerging globally. In de-
veloped nations, frameworks often emphasize a multidisciplinary
approach. For instance, the AI4K12 Initiative in the United States
proposes ve big ideas for K-12 AI education: perception, repre-
sentation and reasoning, learning, natural interaction, and societal
impact as per Touretzky et al., 2019 [16]. This framework aims
to provide a comprehensive understanding of AI, from technical
concepts to ethical considerations. In Europe, the AI4EU project
has developed a curriculum framework that focuses on four main
areas: AI and Machine Learning foundations, AI ethics and gover-
nance, AI systems development, and AI applications as per Foano
et al., 2022 [21]. This approach seeks to balance technical skills
with critical thinking about AI’s societal implications. However, in
developing nations, AI education frameworks are often less com-
prehensive and face signicant implementation challenges. A study
by Pedró et al., 2019 [13], found that many developing countries
struggle to integrate AI concepts into their curricula due to re-
source constraints and a lack of tailored frameworks that consider
local contexts. Current approaches to integrating AI concepts into
curricula vary widely. Some countries have opted for standalone
AI courses, while others integrate AI topics into existing subjects
like computer science or mathematics. For example, China has
introduced AI courses in primary and secondary schools, focus-
ing on practical applications and programming skills as per Su &
Yang, 2022 [22]. The eectiveness of these approaches is still being
evaluated, but early studies suggest that integrated, project-based
learning approaches may be more eective in developing AI literacy
as per Holmes et al., 2019 [2]. However, there are signicant gaps
in existing frameworks, particularly for developing nations. These
include a lack of focus on local context and applications, insucient
emphasis on ethical considerations specic to developing countries,
and limited guidance on implementation in resource-constrained
environments.
2.5 Role of Educators and Teacher Training
Educators play a crucial role in fostering AI literacy, but many face
signicant challenges, especially in developing nations. A survey by
UNESCO, 2019 [34], found that the majority of teachers in develop-
ing countries feel unprepared to teach AI concepts. This lack of pre-
paredness stems from several factors, including insucient training
opportunities, limited access to resources, and rapid technological
changes. The importance of teacher training in AI literacy cannot
be overstated. As per Chai et al., 2020 [23] argue that eective AI ed-
ucation requires teachers who not only understand AI concepts but
can also contextualize them for their students and guide discussions
on ethical implications. However, developing such expertise among
teachers in resource-constrained environments presents signicant
challenges. In many developing nations, teachers face additional
hurdles such as large class sizes, limited technology access, and
competing educational priorities. A study by as per Quah, 2019 [24],
in Southeast Asian countries found that while teachers recognized
the importance of AI literacy, they struggled with practical imple-
mentation due to a lack of training, insucient infrastructure, and

ICETC 2024, September 18–21, 2024, Porto, Portugal Kathala KK and Palakurthi SP
absence of localized teaching materials. To address these challenges,
some initiatives focus on innovative teacher training approaches.
For instance, the AI-Teacher project in India uses a cascading model
where trained teachers become mentors for their peers, helping to
scale up AI literacy education despite resource constraints as per
Schi, 2021 [12] and Chan & Zhou, 2023 [25].
3 Implementation Strategies
The following are the proposed strategies for the developing na-
tions.
•
Various strategies have been proposed and implemented to
enhance AI literacy globally. Policy initiatives play a crucial
role in setting the stage for AI education. Countries like Fin-
land have incorporated AI literacy into their national educa-
tion strategies, emphasizing lifelong learning and workforce
development as per Finland AI Strategy Report, n.d. [26].
•
Public-private partnerships are essential for bridging the
AI opportunity gap. They enhance workforce productivity
through structured, role-based upskilling, create reskilling
pathways using industry micro-credentials, provide high-
quality education to mitigate misinformation and bias, and
oer personalized learning experiences to diverse popula-
tions, breaking language barriers and making education
more accessible globally as per Maggioncalda, 2024 [27].
•
Online platforms and Massive Open Online Courses (MOOCs)
have shown promise in democratizing access to AI educa-
tion. Platforms like Coursera and edX oer AI courses from
top universities, potentially reaching learners in developing
nations. However, challenges remain in terms of internet
access, language barriers, and adapting content to local con-
texts as per Bulathwela et al., 2021 [28] & Bonami et al., 2020
[20].
•
The eectiveness of these strategies varies. A case study of
Rwanda’s partnership with Carnegie Mellon University to
establish a master’s program in AI demonstrates how in-
ternational collaborations can build local capacity as per
McSharry, 2023 [29]. However, the scalability and sustain-
ability of such initiatives remain challenges.
•
In India, the introduction of AI modules in secondary schools
has shown promising results in increasing AI awareness,
but faces challenges in terms of teacher preparedness and
infrastructure as per Goswami & Sharma, 2024 [30].
Lessons learned from these implementations emphasize the im-
portance of:
•
Contextualizing AI education to local needs and challenges
•Focusing on teacher training and support
•Leveraging partnerships to overcome resource constraints
•
Balancing technical skills with ethical and societal consider-
ations
In conclusion, while progress has been made in developing AI lit-
eracy frameworks and implementation strategies, signicant work
remains to adapt these approaches to the unique contexts of devel-
oping nations. Future eorts should focus on creating exible, cul-
turally relevant frameworks that can be implemented with limited
resources while still providing comprehensive AI literacy education.
4 Impact Assessment and Measurement
Assessing the impact of AI literacy programs is crucial for un-
derstanding their eectiveness and guiding future improvements.
Existing studies employ various methods and metrics to evaluate
these programs. As per Dutta & Lanvin, 2019 [31], propose a frame-
work that measures AI literacy across four dimensions: knowledge,
skills, attitudes, and behaviors. This multifaceted approach provides
a comprehensive view of an individual’s AI literacy.
•
Quantitative metrics often include pre- and post-program
tests to measure knowledge gains, as well as surveys to assess
changes in attitudes towards AI. For instance, As per Long
& Magerko, 2020 [5] used a combination of multiple-choice
questions and open-ended problem-solving tasks to evaluate
AI literacy levels among students.
•
Qualitative methods, such as interviews and focus groups,
are also valuable in capturing nuanced understandings and
experiences. As per Kong et al., 2024 [32] employed semi-
structured interviews to explore how participants applied AI
concepts in real-world scenarios after completing a literacy
program.
•
Long-term impact assessment is particularly important in
understanding the broader benets of AI literacy. As per
Holmes et al., 2019 [2], argue that the true value of AI liter-
acy extends beyond immediate knowledge gains to include
improved decision-making, enhanced problem-solving skills,
and increased adaptability in an AI-driven world. However,
conducting long-term studies in developing nations often
poses challenges due to resource constraints and participant
attrition.
5 Need for Comprehensive Framework
The unique challenges faced by developing nations in promoting
AI literacy necessitate a tailored framework. Existing frameworks
often assume a level of technological infrastructure and educational
resources that may not be available in many developing countries
as per Nye, 2014 [9]. Additionally, these frameworks may not ade-
quately address the specic cultural, economic, and social contexts
of developing nations.
A comprehensive framework for developing nations should con-
sider:
•Resource constraints and infrastructure limitations
•Cultural and linguistic diversity
•Local economic priorities and workforce needs
•Existing educational systems and curricula
This paper aims to address this gap by proposing a comprehen-
sive AI literacy framework specically designed for developing
nations. The framework will focus on essential competencies and
skills, curriculum integration strategies, and the pivotal role of
educator training. Furthermore, the paper will outline strategic
implementation plans, taking into account the unique challenges
and opportunities present in developing nations.

AI Literacy Framework and Strategies for Implementation in Developing Nations ICETC 2024, September 18–21, 2024, Porto, Portugal
6 Conclusion & Future Scope
This review has highlighted the growing importance of AI literacy
in the global context and the signicant disparities that exist be-
tween developed and developing nations. Key ndings include the
multifaceted nature of AI literacy, encompassing technical knowl-
edge, ethical considerations, and practical skills. Challenges faced
by developing nations in implementing AI literacy programs in-
clude limited infrastructure, a lack of trained educators, and cur-
riculum gaps. However, innovative strategies such as public-private
partnerships and online platforms show potential in addressing
these challenges. It is crucial to contextualize AI education to local
needs and cultural contexts. There is a need for comprehensive
impact assessment methods to guide program improvements and
demonstrate long-term benets.
To build upon this work, future research could focus on: Devel-
oping and validating culturally appropriate assessment tools for
measuring AI literacy in diverse contexts. Conducting longitudinal
studies to understand the long-term impacts of AI literacy programs
in developing nations. Exploring innovative, low-cost methods for
delivering AI education in resource-constrained environments. In-
vestigating the role of AI literacy in promoting economic devel-
opment and social equity in developing nations. Examining the
intersection of AI literacy with other critical skills such as digital
literacy and data science competencies. These future directions
can further contribute to bridging the global AI literacy divide and
ensuring that the benets of AI technologies are accessible to all.
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