Research on artificial intelligence literacy has exploded since 2018, with domestic Chinese studies focusing on K-12 and teacher education while international research emphasizes university-level AI literacy development.

Objective: This study aimed to conduct a comprehensive bibliometric analysis of artificial intelligence literacy research trends from 2018-2025, examining research hotspots, developmental patterns, and differences between domestic Chinese and international perspectives using visualization tools. The researchers sought to map the evolution of AI literacy as an academic field and provide insights for future educational policy and practice.

Methods: The researchers collected 484 core publications from China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) databases, focusing on topics related to "artificial intelligence literacy" and "intelligence literacy" between 2018-2025. Using CiteSpace 6.3.R4 visualization software for bibliometric analysis, they conducted keyword co-occurrence analysis, burst detection analysis, and timeline clustering analysis. The study employed systematic data cleaning procedures to exclude conference papers, book reviews, and duplicate publications, resulting in 269 Chinese articles and 215 English articles. Key parameters included yearly time slicing, focus on author/institution/keyword node types, g-index selection criteria, and network pruning methods to optimize visualization.

Key Findings:

• Publication volume showed significant year-over-year growth, with explosive increases between 2022-2025 compared to steady growth from 2018-2021, reflecting AI technology's rapid advancement and widespread application.
• Keyword analysis revealed distinct research emphases: CNKI (Chinese) research focused on "Artificial Intelligence," "Intelligence Literacy," "Pre-service Teachers," and elementary education applications, while WOS (international) research emphasized "AI Literacy," "Artificial Intelligence Literacy," and higher education contexts.
• Timeline clustering analysis identified 7 clusters in CNKI and 9 clusters in WOS databases, with high modularity Q-values (0.6868 for CNKI, 0.538 for WOS) and mean silhouette values (0.9458 for CNKI, 0.9032 for WOS), indicating robust clustering structures.
• Burst detection analysis highlighted emerging trends: domestic research showed sustained attention to "Intelligence Literacy," "Teacher Education," and "Core Competencies," while international research revealed growing focus on "early childhood education," "large language models," and "K-12 AI education."
• Network density analysis (0.0156 for CNKI, 0.0166 for WOS) demonstrated the multi-dimensional nature of AI literacy research, encompassing technical skills, pedagogical approaches, and ethical considerations.

Implications: The findings provide crucial insights for global AI literacy education development. The research demonstrates that AI literacy has evolved from a nascent concept to a critical educational priority, with different regions emphasizing different educational levels and approaches. The study supports the need for comprehensive AI literacy frameworks that address multiple educational stages, from elementary through higher education. The identified research hotspots and trends offer valuable guidance for educational policymakers, curriculum designers, and researchers in developing effective AI literacy programs. The divergent focus between domestic and international research suggests opportunities for knowledge exchange and collaborative development of more comprehensive AI literacy educational models.

Limitations: The study has several notable limitations. The research was limited to Chinese and English language publications, potentially excluding valuable insights from other linguistic communities. The focus on two specific databases (CNKI and WOS) may not capture the complete global research landscape. The bibliometric analysis approach, while comprehensive for mapping trends, cannot assess the quality or practical effectiveness of the research approaches identified. The timeframe (2018-2025) may be too recent to capture long-term educational outcomes or impacts of AI literacy initiatives. Additionally, the rapid pace of AI technology development means that some findings may quickly become outdated as new technologies and educational approaches emerge.

Future Directions: The researchers recommend several critical areas for future investigation. Longitudinal studies should track the effectiveness of different AI literacy educational approaches across various educational levels. Future research should expand to include more diverse linguistic and cultural contexts to develop truly global perspectives on AI literacy education. Studies should focus on developing and validating standardized assessment frameworks for AI literacy competencies. Research is needed on the practical implementation challenges of AI literacy curricula and the professional development requirements for educators. Additionally, future work should explore the ethical dimensions of AI literacy education more deeply, particularly regarding digital equity and access. The study also suggests the need for interdisciplinary collaboration to address the complex, multi-faceted nature of AI literacy education effectively.

Title and Authors: "Research Hotspots, Trends, and Implications of Artificial Intelligence Literacy Based on CiteSpace" by XiaoXia Tian, YuFei Zhou, and Rui Du.

Published On: 2025 (Volume 3, Issue 4)

Published By: World Journal of Information Technology (Print ISSN: 2959-9903, Online ISSN: 2959-9911)