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environments (Holmes et al., 2023; Kasneci et al., 2023). Furthermore, distance digital
ecosystems provide a conducive context for embedding AI literacy development directly
into learning processes, enabling learners to acquire competencies through authentic and
practice-based experiences (Ng et al., 2021; Asrifan et al., 2025).
2.3. AI literacy and workforce resilience
The increasing integration of AI technologies into the workplace has intensified the
need to understand the relationship between AI literacy and workforce resilience (World
Economic Forum, 2023; McKinsey Global Institute, 2023). Theoretical perspectives such as
creative destruction explain how technological innovation simultaneously disrupts
existing job structures while creating new opportunities (Schumpeter, 1942). Task-based
labor models further demonstrate that automation reshapes the nature of work by
shifting human labor toward non-routine, higher-order cognitive tasks (Autor et al., 2024;
Acemoglu & Restrepo, 2019).
Within this context, AI literacy emerges as a critical adaptive capability that enables
individuals to transition from displacement to augmentation in AI-driven environments
(Liu et al., 2025; McKinsey Global Institute, 2023). Empirical evidence suggests that
integrating AI competencies with digital literacy enhances productivity, problem-solving
ability, and adaptability in the workplace (IBM Institute for Business Value, 2023).
Moreover, psychological constructs such as digital confidence and learning agility have
been identified as key mediating factors that strengthen workforce resilience (World
Economic Forum, 2023). These findings indicate that resilience is not solely a function of
technical expertise but also depends on continuous learning capacity and the ability to
collaborate effectively with intelligent systems (Liu et al., 2025).
2.4. Policy and institutional perspectives on AI literacy
The integration of AI literacy into education systems requires coordinated efforts
across institutional, national, and global policy levels (OECD, 2023; UNESCO, 2023). At the
institutional level, universities are increasingly positioned as key actors in developing AI-
ready graduates through curriculum innovation, faculty development, and investment in
digital infrastructure (Shelton & Dockens, 2025; Holmes et al., 2023). However, without
alignment with broader policy frameworks and labor market needs, such initiatives risk
fragmentation and limited scalability (Liu et al., 2025; OECD, 2023).
Recent policy frameworks emphasize the importance of ethical governance, data
protection, educator empowerment, inclusivity, and equitable access to AI technologies
in education (European Commission, 2026; UNESCO, 2023). Public-private partnerships
and cross-sector collaboration have also been identified as critical mechanisms for
ensuring that AI literacy aligns with evolving workforce demands (World Economic Forum,
2023; OECD, 2023). Furthermore, integrating AI literacy into general education curricula
across disciplines has been proposed as a strategic approach to democratizing access to
AI competencies (Ng et al., 2021; Holmes et al., 2023). Collectively, these policy directions
reinforce AI literacy—alongside foundational data literacy—as a foundational component
of sustainable digital transformation and lifelong learning systems (UNESCO, 2023;
European Commission, 2026).
3. Research methodology
3.1. Data sources and collection
This study employs a qualitative, conceptually grounded research design, grounded
in systematic literature synthesis, to develop a Digital Learning Ecosystem Model to
enhance Artificial Intelligence Literacy (AI Literacy). The study relies on secondary data

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