Conceptualizing Digital Literacy for the AI Era: A Framework for Preparing Students in an AI-Driven World
Introduction: As artificial intelligence (AI) has become increasingly integrated into daily life, traditional digital literacy frameworks must be revised to address the modern challenges. This study aimed to develop a comprehensive framework that redefines digital literacy in the AI era by focusing on the essential competencies and pedagogical approaches needed in AI-driven education. Methods: This study employed a constructivist and connectivist theoretical approach combined with Jabareen's methodology for a conceptual framework analysis. A systematic literature review from 2010-2024 was conducted across education, computer science, psychology, and ethics domains, using major databases including ERIC, IEEE Xplore, and Google Scholar. The analysis incorporated a modified Delphi technique to validate the framework’s components. Results: The developed framework comprises four key components: technical understanding of AI systems, practical implementation skills, critical evaluation abilities, and ethical considerations. These components are integrated with traditional digital literacy standards through a meta-learning layer that emphasises adaptability and continuous learning. This framework provides specific guidance for curriculum design, pedagogical approaches, assessment strategies, and teacher development. Conclusions: This framework offers a structured approach for reconceptualising digital literacy in the AI era, providing educational institutions with practical guidelines for implementation. Integrating technical and humanistic aspects creates a comprehensive foundation for preparing students for an AI-driven world, while identifying areas for future empirical validation.
Authors
- FX. Risang Baskara (ORCID: https://orcid.org/0000-0001-7662-3288)
Institutions
- Sanata Dharma University (ID)
Publication Details
- Journal
- Data & Metadata
- Published
- 2024-12-04
- DOI
- https://doi.org/10.56294/dm2025530
- Citations
- 17
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 7.00