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computational tools augment, rather than replace, human critical analysis while
maintaining strict human agency, oversight, and data privacy (UNESCO, 2023; European
Commission, 2026).
Overall, integrating thematic synthesis and conceptual modeling provides a robust
methodological foundation for developing the proposed Digital Learning Ecosystem
Model, ensuring theoretical coherence and practical relevance for policy and educational
implementation.
4. Results and discussion
4.1. The AI literacy ecosystem model
The findings of this study are presented in a synthesized conceptual framework, the AI
Literacy Ecosystem Model. As illustrated in Figure 1, Artificial Intelligence Literacy (AI Literacy) is
positioned at the core of the model, comprising four interrelated dimensions:
Cognitive/Epistemic, Applied/Technical, Ethical/Critical, and Socio-emotional. These dimensions
collectively represent the essential competencies required for individuals to effectively engage
with AI technologies in the digital economy (Ng et al., 2021; Holmes et al., 2023). In alignment
with the European Commission’s (2026) mandate for human-centric and trustworthy AI, the
Ethical/Critical dimension is particularly emphasized as foundational to ensuring that learners
can navigate AI systems safely, equitably, and with full awareness of algorithmic transparency
and data privacy.
Surrounding the core is the Distance Digital Learning Ecosystem, which functions as an
enabling environment that integrates technology, pedagogy, and learner support mechanisms.
This ecosystem layer emphasizes the role of learner support systems in facilitating continuous,
personalized, and accessible learning, particularly within distance education contexts. The
circular structure of the ecosystem indicates a dynamic, iterative process in which learning,
application, and feedback continuously reinforce the development of AI literacy (Shelton &
Dockens, 2025; Selwyn, 2023).
The model further incorporates a multi-level structural framework comprising micro,
meso, and macro levels. At the micro level, AI literacy is operationalized through curriculum
design and instructional practices that directly influence learners’ competencies. At the meso
level, institutional strategies—such as digital infrastructure, faculty development, and
organizational policies—support the implementation and scalability of AI literacy initiatives. At
the macro level, national policies and regulatory frameworks provide direction and alignment
with broader socio-economic goals (OECD, 2023; UNESCO, 2023).
Additionally, the model highlights key outcomes emerging from the interaction between
AI literacy and the digital learning ecosystem. These include workforce resilience, lifelong
learning, and alignment with the Sustainable Development Goals (SDGs). This outcome layer
reflects the broader societal impact of AI literacy, extending beyond individual competencies to
contribute to sustainable economic and social development and ensuring a fair, inclusive digital
transition that leaves no one behind (World Economic Forum, 2023; European Commission,
2026).
Overall, the model shows that AI literacy should not be conceptualized as a standalone
skill set but as a dynamic, integrative competency embedded within a multi-layered digital
learning ecosystem. The interconnections among the four dimensions, the supporting
ecosystem, and policy levels demonstrate that effective AI literacy development requires a
holistic and systemic approach. The overall structure and relationships among these
components are visually represented in Figure 1.

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