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Artificial Intelligence (AI) has become a defining force in education, transforming how knowledge is created,
evaluated, and shared. However, universities still face challenges in integrating AI literacy effectively into their
curricula. Current efforts, opinions, and ideas vary widely. Some concentrate on technical skills, while others
emphasise ethics or social impacts, often without pedagogical alignment or supporting research. This study offers
a Structured Literature Review (SLR) of 87 peer-reviewed sources (2018–2025) from Scopus-indexed
journals, including Computers & Education, AI & Society, British Journal of Educational Technology, Nature
Machine Intelligence, and the International Journal of Artificial Intelligence in Education. The SLR synthesises
conceptual, pedagogical, and governance perspectives to create the AI Literacy Design Matrix (AILDM). This
matrix serves as a comparative framework identifying four interconnected curriculum design dimensions:
conceptual, ethical, productive, and participatory. The review reveals that, although higher education worldwide
acknowledges the importance of AI literacy, most programmes remain fragmented, with limited integration of
ethics or civic aspects. The AILDM proposes a framework for designing balanced curricula that foster
understanding, creativity, and responsibility. The paper concludes that AI literacy should be regarded as a civic
and epistemic infrastructure, and therefore integrated across disciplines rather than confined to computer science.
AI literacy; higher education; curriculum design; structured literature review; comparative
framework; digital transformation; ethics in AI.
Artificial Intelligence is not a peripheral technology. It has become a pervasive condition of academic life that
can’t be ignored or eliminated. Algorithms increasingly shape research discovery, administrative decision-
making, and pedagogical interaction (Selwyn 2022; Williamson 2023). Higher education faces an epistemic shift
as a result of this. Consequently, students and faculty now inhabit an ecosystem where knowledge is co-
constructed with intelligent systems. The capacity to understand, critique, and co-create with these systems
defines a new form of literacy, namely (Long & Magerko 2020; Ng et al. 2021). However, no AI
system operates without a human prompt. Whilst enthusiasm for AI integration in universities is strong,
conceptual coherence is weak. Governments and professional bodies have begun issuing frameworks—
UNESCO (2023), OECD (2021), European Commission (2022). Notwithstanding, the different local
implementations vary dramatically. Some institutions embed AI ethics into general-education modules (Holmes
et al. 2019), whilst others launch coding boot camps, and many remain in exploratory stages. Without alignment
between philosophical values, technical capability, ethical reflection, and civic engagement, “AI literacy” risks
becoming a buzzword detached from learning outcomes.
The objective of this paper is therefore to for curriculum design in higher
education and to add at least a window of clarity in the evolving schools of thought surrounding AI literacy. It
does so through a analysing 87 academic sources published between
2018 and 2025 across leading education and technology journals.
Three questions guide the study:
1. How is AI literacy conceptualised in recent higher-education scholarship?
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2. What pedagogical and ethical models underpin existing curricula?
3. How can comparative analysis inform a coherent framework for curriculum design?
The review synthesises these findings into the —a tool that helps
educators balance conceptual, ethical, productive, and participatory learning goals. By articulating these
dimensions, the paper extends current scholarship beyond fragmented perspectives toward an integrative,
actionable framework.
The genealogy of AI literacy can be traced through the evolution of digital and data literacies. Early theorists
such as Gilster (1997) and Bawden (2008) defined digital literacy as the capacity to locate, evaluate, and create
information using digital technologies. As big data emerged, attention shifted to “
is the ability to interpret and communicate with data critically (Wolff et al. 2016; Prado & Marzal 2013). AI
literacy extends this trajectory into systems that learn and act, demanding epistemic understanding of algorithms
and their societal consequences (Long & Magerko 2020; Ng et al. 2021).
Luckin et al. (2016) argued that education must move from learning with AI to learning about AI. Holmes,
Bialik and Fadel (2019) later proposed four pillars: knowledge, skills, character, and meta-learning, contending
that AI literacy must cultivate ethical and adaptive reasoning alongside technical ability. Floridi (2019) and
Dignum (2019) positioned AI literacy as a moral and epistemological competence, suggesting an awareness of
how information systems shape human agency. Druga et al. (2023) found that students frequently
anthropomorphise AI, attributing autonomy where none exists. Such misconceptions underline the need for
curricula that teach not only what AI does but how it knows. In this sense, AI literacy merges cognitive insight
with philosophical reflexivity.
AI literacy education draws upon constructivist and socio-cultural pedagogies that emphasise active, contextual
learning (Vygotsky 1978; Bruner 1966). Students learn effectively when they engage with AI tools to solve
authentic problems, reflect on outcomes, and iterate, thus mirroring AI’s own learning processes (Holmes et al.
2019). Design-based research demonstrates that project-centred approaches encourage deeper conceptual
understanding (Chan et al. 2021; Nørgård 2022). For example, Amershi et al. (2019) recommend human-AI
collaboration projects where learners co-design interfaces or evaluate model outputs. Such experiential
strategies bridge abstract theory with ethical application.
However, pedagogy must address the affective dimension as well. Students often experience both fascination
and anxiety toward AI (Henrickson 2021). Educators must therefore foster emotional resilience and moral
reasoning, guiding learners to see AI as a tool for empowerment rather than a threat (Shneiderman 2022; Selwyn
2022). Integrating reflection journals, ethical debates, and peer evaluation helps balance technical confidence
with ethical sensitivity.
International frameworks increasingly specify AI literacy goals for education systems. The UNESCO Guidance
for Generative AI in Education and Research (2023) highlights human-centred design and equitable access.
The OECD Digital Education Outlook (2021) supports lifelong learning policies that incorporate AI literacy
across different disciplines. The integrates AI-related skills into
the wider digital-skills framework, connecting them with critical thinking and citizenship.
At the institutional level, initiatives such as the (2020)
and (Luckin 2021) demonstrate cross-disciplinary
curriculum design. In Asia, the integrates
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ethics and entrepreneurship (Lim et al. 2021). Meanwhile, Nordic universities emphasise transparency and
explainability, reflecting regional commitments to participatory governance (Hakkarainen & Hietajärvi 2022).
Despite these advances, gaps still exist. Williamson (2023) and Selwyn (2022) point out that many universities
treat AI ethics as optional, separate from core curricula. Professional-development gaps among faculty further
impede adoption (Chan & Yuen 2023). The literature consistently advocates for a holistic approach, where AI
literacy is embedded within institutional culture, assessment design, and teacher training (Chan et al. 2021;
OECD 2023).
Beyond pedagogy, AI literacy encompasses ethical awareness and civic and academic engagement. Scholars
such as Rahwan (2018), Benjamin (2019), Noble (2018), and Buolamwini & Gebru (2018) emphasise
algorithmic bias and systemic inequality, arguing that critical literacy must involve an understanding of power.
Ruha Benjamin’s Race After Technology (2019) and Safiya Noble’s Algorithms of Oppression (2018)
demonstrate how AI systems perpetuate social hierarchies. These insights expand literacy from individual skills
to collective responsibility. Floridi (2019) introduces the notion of the , in which human and artificial
agents co-create informational realities; here, literacy implies ethical navigation within this shared environment.
Similarly, Crawford’s Atlas of AI(2021) exposes the environmental and labour costs of AI, linking literacy to
sustainability ethics. Scholars agree on three key principles: transparency, accountability, and participation
(Whittlestone et al. 2019; Jobin et al. 2019). In higher education, this means developing curricula that enable
students to challenge datasets, critique algorithmic decisions, and participate in policy discussions.
Across these studies, two persistent issues emerge:
1. : AI literacy initiatives often address only one dimension (technical or ethical) without
integrating others.
2. : Only a few frameworks systematically examine cross-regional curricular
differences.
The present review addresses both aspects by analysing 87 peer-reviewed studies through an integrative
comparative lens, resulting in the AI Literacy Design Matrix (AILDM) as a unified model.
This study employed a to ensure transparency and replicability (Denyer
& Tranfield, 2009; Snyder, 2019). The SLR synthesised findings from
published between 2018 and 2025 in Scopus-indexed journals. Following Torraco (2005) and Boell &
Cecez-Kecmanovic (2015), the process combined systematic search strategies with interpretive synthesis to
integrate theoretical, empirical, and pedagogical insights.
Searches were conducted across
using combinations of the keywords:
“AI literacy,” “artificial intelligence education,” “curriculum design,” “higher education,” “ethical AI,” and
“learning analytics.” The corpus included studies from journals such as Computers & Education, AI &
Society, British Journal of Educational Technology, Educational Technology Research &
Development, Computers in Human Behaviour, Ethics and Information Technology, and Nature Machine
Intelligence.
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Peer-reviewed articles focusing on AI literacy, higher education, or curriculum development.
Studies offering theoretical frameworks, empirical analyses, or pedagogical interventions.
Publications in English between 2018 and 2025.
Non-academic commentaries or media pieces.
Papers limited to K-12 education unless transferable to tertiary contexts.
Technical AI research without educational relevance.
Initial search results (n = 1,142) were screened by title and abstract. Duplicates and irrelevant studies were
removed, leaving 132 for full-text review. After quality appraisal using the Critical Appraisal Skills Programme
(CASP 2020) checklist, 87 studies met the inclusion criteria. Data were coded inductively with NVivo, resulting
in four thematic clusters—conceptual, ethical, productive, and participatory. These clusters informed the
AILDM framework. Cross-comparison by geographic region (Europe, North America, Asia-Pacific, Africa)
revealed contextual patterns and pedagogical trends.
The SLR is interpretive rather than comprehensive; its strength lies in conceptual integration. An English-
language bias and focus on peer-reviewed literature may under-represent regional initiatives documented in grey
sources. Nonetheless, triangulation across 87 academic studies improves robustness and validity. The inclusion
of multiple journals and methodologies ensures a balanced synthesis across technical, ethical, and pedagogical
dimensions.
The synthesis of 87 peer-reviewed sources resulted in the AI Literacy Design Matrix (AILDM)—a
comparative framework that outlines four interconnected curriculum design dimensions: conceptual, ethical,
productive, and participatory literacy. Each dimension represents a distinct yet overlapping area of learning
outcomes, pedagogical strategies, and assessment methods.
Develop fundamental
understanding of how AI
systems operate.
Explain algorithmic reasoning;
identify dataset bias; describe
learning types (supervised,
unsupervised, reinforcement).
Cross-disciplinary theory
modules; case studies linking AI
to disciplinary contexts; data
visualisation tasks.
Cultivate reflective and
moral awareness of AI’s
societal implications.
Evaluate ethical dilemmas; debate
fairness and accountability; apply
ethical frameworks to real-world
cases.
Ethics workshops; simulation of
policy decisions; reflective
essays and scenario-based
discussions.
Enable creative and
competent use of AI
tools for problem-
solving and innovation.
Build or adapt AI systems
responsibly; use generative tools
critically; interpret AI outputs
accurately.
Project-based labs; hackathons;
interdisciplinary design
challenges.
Empower engagement
in governance and
public discourse on AI.
Critique AI policy; propose
institutional guidelines; collaborate
in citizen-AI governance projects.
Policy simulations; community-
based research; debate forums
and stakeholder dialogues.
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Conceptual literacy encompasses what AI is and how it functions. It includes knowledge of computational logic,
model architectures, and the epistemological assumptions underpinning machine reasoning (Mitchell 2019;
Jordan 2019). Higher education should view AI understanding as part of overall epistemic competence, akin to
scientific literacy (Chin et al. 2023).
Interdisciplinary teaching is essential: social science students should grasp the basics of algorithmic decision-
making, while computer science students should explore the philosophical foundations of cognition and bias.
This reciprocity helps bridge the cognitive divide between technical and humanistic disciplines (Nørgård 2022;
Holmes et al. 2019).
Ethical literacy broadens understanding into the moral domain. According to Dignum (2019) and Floridi (2019),
this involves not only following ethical codes but also developing moral imagination. Students must analyse
AI’s role in perpetuating or reducing inequity (Benjamin 2019; Noble 2018; Buolamwini & Gebru 2018).
Curricula should incorporate a dialogic pedagogy, enabling learners to debate, empathise, and reflect.
Approaches such as “ethics sandboxes” (Borenstein et al. 2021) and structured moral reasoning frameworks
(Lau & Wong 2023) allow students to engage with complex scenarios without fixed answers. Ethical literacy,
therefore, transforms AI from a technical topic into a humanistic inquiry—grounding decision-making in
empathy, justice, and social context.
Productive literacy refers to agency through practice—the ability to engage AI tools critically and creatively
(Amershi et al. 2019; Shneiderman 2022). Unlike digital skills training, it emphasises reflexivity: using AI to
enhance human capability while maintaining interpretive control (Chan et al. 2021). Hands-on, project-based
learning closely aligns with this dimension. For instance, media students might use generative AI to analyse bias
in content production; engineering students could apply machine learning to sustainable energy modelling.
Productive literacy bridges knowing and doing, ensuring that creative experimentation is guided by ethical
awareness.
Participatory literacy relates to AI’s civic dimension—the ability to understand and influence governance,
policy, and cultural narratives (Rahwan 2018; West et al. 2019). It corresponds with Freire’s (1970) idea
of critical consciousness: education as a tool for empowerment through dialogue. Universities can foster this
through policy debates, hackathons, and community engagement projects that focus on local AI concerns, such
as surveillance or algorithmic hiring. Participatory literacy also connects academic research to public discussion,
enabling graduates to act as informed citizens who co-govern technology rather than passively consume it.
Conceptual Literacy: Students can examine the fundamentals of algorithmic reasoning and dataset bias
through brief, accessible modules delivered collaboratively by computer science and sociology lecturers.
Case studies on algorithmic discrimination (e.g., biased facial recognition systems)
can be utilised for structured debates and reflective essays.
Productive Literacy: Students can employ open-source AI tools to analyse real-world datasets and
apply their learning to social-impact issues.
The class can end with a simulated policy hearing during which students present
recommendations to a mock ethics board.
Impact: Demonstrates how AI ethics are practical and illustrates the connection between technical and moral
reasoning.
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Teams can investigate model architectures for energy-efficient computing.
Each team can justify the environmental trade-offs of their chosen model using the
EU’s AI Ethics Guidelines.
Students can develop prototype solutions that optimise power usage or data
storage.
Results can be shared with local industry partners or municipal sustainability
councils for feedback.
Impact: Linking AI design to sustainability and civic responsibility, illustrating how technical skill and ethical
responsibility can coexist.
The AI Literacy Design Matrix (AILDM) is inherently adaptable and can evolve in response to emerging
technologies that continue to reshape higher education. As generative AI, adaptive learning platforms, and
multimodal systems become more widespread, the framework can include new literacies such as meta-literacy.
This is the ability to reflect on one’s interactions with intelligent systems and to develop sustainability literacy,
connecting AI innovation to ethical, environmental, and social responsibility. Ongoing collaboration among
educators, technologists, and policymakers will help ensure that the AILDM remains responsive to advances in
artificial intelligence, keeping AI literacy education relevant, resilient, and future-proof.
The structured review identified four global clusters demonstrating how AI literacy is conceptualised and
implemented in higher education.
Europe leads in governance-led models. The European Commission’s AI Act (2024) and Digital Education
Action Plan (2021–2027) embed human-centred AI within higher education. Scandinavian universities
exemplify ethical integration practices—embedding AI ethics into teacher training (Hakkarainen & Hietajärvi,
2022)—while Mediterranean systems lag in implementation (OECD, 2023). However, overemphasis on
regulation risks bureaucratising creativity. Critics (Williamson 2023; Nørgård 2022) warn that compliance
cultures may stifle innovation. The AILDM provides balance by linking ethical oversight with participatory
learning.
The U.S. features a fragmented yet vibrant ecosystem. Institutions like MIT, Stanford, and Georgia Tech promote
experiential, design-based learning (Amershi et al. 2019; Dede 2022). Ethical reasoning, however, remains
inconsistently integrated outside elite contexts (Selwyn 2022). Federal initiatives such as AI and the Future of
Teaching and Learning (U.S. Department of Education 2023) encourage responsible use but stop short of
defining curriculum standards. This pluralism fosters innovation but undermines systemic coherence.
Comparative synthesis reveals a need for national coordination and shared benchmarks.
Asian models emphasise technical mastery and innovation capacity. China’s AI Education Strategy (Zeng
2022) and Singapore’s AI for Everyone programme (Lim et al. 2021) highlight STEM integration and
entrepreneurship. Ethical discourse, while emerging, remains secondary (Chan & Yuen, 2023). Korean and
Japanese universities have begun introducing AI humanities modules, bridging cultural values of harmony with
technical excellence (Lee 2023). The AILDM’s participatory dimension could further localise ethics and
governance in these contexts, enhancing public dialogue on AI’s social effects.
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In Africa and Latin America, AI literacy is connected to development and inclusion. The African Union Digital
Transformation Strategy (2020–2030) describes AI education as a key driver of equity. Case studies from South
Africa (Goosen 2024) and Brazil (Ferreira 2023) show how universities adapt open-source AI tools to local
languages and economies. These initiatives, although resource-limited, focus on community participation and
building teacher capacity—aligning naturally with the AILDM’s participatory ethos. The challenge is to scale
such models without creating dependency on imported frameworks.
The global evidence shows that AI literacy efforts remain isolated—focused on technical aspects in the U.S. and
Asia, ethical considerations in Europe, and development in the Global South. The AILDM unites these strands
through a multi-dimensional integration approach: conceptual (knowledge), moral (values), productive (skills),
and participatory (agency). Embracing this framework encourages universities to align course outcomes,
assessments, and institutional policies. Similar integrated models have transformed education in sustainability
and digital citizenship (Tilbury 2011; Redecker 2020); AI literacy now demands a comparable shift.
Practical execution requires integrating it into the curriculum rather than treating it as an add-on. Recommended
approaches include:
: audit existing programs for implicit AI content and align to AILDM dimensions.
: pair data-science faculty with humanities scholars to design integrative
modules.
: use reflective portfolios and project outcomes rather than closed-book exams
(Biggs & Tang, 2011).
: institutions can link AI literacy indicators to graduate attributes or to
professional body standards.
Faculty readiness remains a key challenge. Studies (Chan et al., 2021; OECD, 2023) show that many lecturers
feel underprepared for AI pedagogy. Professional learning communities and mentorship models can develop
capacity (Dede 2022). Finland’s Elements of AI course illustrates scalable faculty training when paired with
open-access resources. Institutional leadership must regard AI literacy as a strategic infrastructure. Creating AI
education hubs and interdisciplinary chairs encourages sustainable innovation and cross-faculty dialogue
(Holmes et al. 2019; Williamson 2023).
AI ethics cannot be culturally neutral. Western frameworks emphasise autonomy and privacy; Asian traditions
prioritise harmony and collective responsibility (Zeng 2022; Dignum 2019). The AILDM’s flexible architecture
allows weighting among dimensions according to cultural context, ensuring global applicability without moral
imperialism.
Assessment remains a frontier. Current tools assess technical skills but neglect ethics and participation (Ng et al.
2021). Multi-level evaluation should measure:
(conceptual tests, self-assessments),
(ethical reasoning rubrics), and
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(policy proposals, community engagement).
Developing validated instruments, such as the Digital Competence Framework (DigComp 2.2) , could
standardise evaluation while respecting contextual nuance.
This Structured Literature Review consolidates evidence from 87 peer-reviewed studies to propose the AI
Literacy Design Matrix (AILDM) as a comprehensive framework for integrating AI literacy within higher
education. The analysis shows that while global enthusiasm for AI education exists, its implementation remains
fragmented. The AILDM addresses this by encompassing four key dimensions: conceptual, ethical, productive,
and participatory. Each is crucial to fostering graduates who are informed, responsible, and empowered. Future
research should implement the AILDM in pilot curricula and assess its impact on students’ understanding, ethical
judgment, and civic participation.
By translating its four dimensions into practical curriculum design, the AILDM moves beyond theory into
concrete educational practice. Universities can use it to map current courses, identify gaps, and develop new
modules that incorporate AI literacy across disciplines—from ethics-focused engineering projects to reflective
AI-in-society seminars. Faculty can align assessments with each literacy dimension, while institutions can
benchmark graduate competencies against them. In doing so, the framework not only guides curriculum reform
but also fosters a culture of responsible innovation where students learn to create, question, and co-govern
intelligent technologies.
Policymakers should encourage faculty development, interdisciplinary collaboration, and accreditation reforms
to make AI literacy a core skill for all graduates. AI literacy is not merely about learning technology; it is
about learning to live intelligently with intelligence.
is the Chief Executive Officer of the Infomage Rims Group and a researcher specialising
in artificial intelligence, digital transformation, Business Incubation and education policy. His work explores the
intersection of technology, ethics, and contextual awareness in post-school education and training systems.
This research was supported by the , whose commitment to advancing understanding of
AI literacy and education made this study possible.
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