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sources, including peer-reviewed journal articles, academic books, and international
policy reports, to ensure a comprehensive and multidisciplinary perspective (Ng et al.,
2021; Holmes et al., 2023).
Data were collected from major academic databases, including Scopus, Web of
Science, and Google Scholar, to capture a wide range of scholarly contributions related to
AI literacy, digital learning ecosystems, and educational transformation (Bond et al., 2024).
In addition, policy documents from international organizations—such as UNESCO, OECD,
the World Economic Forum, and the European Commission—were incorporated to reflect
global policy directions, ethical guidelines, and workforce trends (UNESCO, 2023; OECD,
2023; World Economic Forum, 2023; European Commission, 2026).
A purposive sampling strategy was applied to select relevant literature published
between 2020 and 2026, ensuring alignment with recent developments in generative AI,
digital education, and updated ethical frameworks (Kasneci et al., 2023; McKinsey Global
Institute, 2023**; European Commission, 2026**). Keywords used in the search process
included “AI literacy,” “digital learning ecosystem,” “generative AI in education,” “lifelong
learning,” and “workforce resilience” (Asrifan et al., 2025).
Inclusion criteria focused on studies that provide theoretical, empirical, or policy-
related insights into AI literacy and digital learning systems, while sources lacking
academic rigor or relevance were excluded (Holmes et al., 2023; Bond et al., 2024). The
selected literature was systematically reviewed to identify key themes, conceptual
constructs, and emerging trends that inform the proposed model.
3.2. Analytical and conceptual framework
The analytical approach of this study is based on qualitative thematic synthesis
combined with conceptual integration to construct a comprehensive framework for AI
literacy within a digital learning ecosystem (Ng et al., 2021; Holmes et al., 2023). The
analysis followed a three-stage process: (1) open coding to extract key concepts from the
literature, (2) axial coding to categorize and connect related concepts, and (3) selective
synthesis to integrate these categories into a coherent conceptual model (Bond et al.,
2024).
Through this process, four core dimensions of AI literacy—Cognitive/Epistemic,
Applied/Technical, Ethical/Critical, and Socio-emotional—were identified and synthesized
as the foundational components of the framework (Ng et al., 2021; Asrifan et al., 2025).
These dimensions were then mapped within a Distance Digital Learning Ecosystem to
illustrate the interaction between individual competencies, learning environments, and
broader socio-economic outcomes, particularly workforce resilience and lifelong learning.
The conceptual framework further integrates multi-level perspectives, including
micro (learner and instructional practices), meso (institutional strategies), and macro
(policy and national systems) levels, to reflect the systemic nature of AI literacy
development and ethical governance (OECD, 2023; UNESCO, 2023**; European
Commission, 2026**). This multi-layered structure enables a holistic understanding of
how AI literacy can be operationalized across educational systems.
To enhance analytical rigor, digital tools were employed to support literature
organization and synthesis. Reference management systems such as Zotero and
Mendeley were used for systematic data management, while AI-assisted tools supported
thematic clustering and conceptual mapping across large datasets (Kasneci et al., 2023;
IBM Institute for Business Value, 2023). The use of AI in the analytical process aligns with
emerging practices and ethical guidelines in AI-supported research, ensuring that

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