AI literacy education conceptualizations vary widely across educational contexts, with recent shifts toward teaching generative AI tools and their responsible use particularly in higher education settings.
Objective: This study aimed to examine how AI literacy is conceptualized and approached in K-12 and higher education, and to identify new trends that have emerged in AI literacy research since the introduction of generative AI.
Methods: The researchers conducted an integrative review of 124 AI literacy studies published between 2020 and 2024, applying inclusion criteria that focused on K-12 and undergraduate educational contexts. The literature was analyzed using a conceptual framework that examines different perspectives on AI (technical detail, tool, and sociocultural) and different approaches to literacy (functional, critical, and indirectly beneficial). The researchers systematically coded studies based on these perspectives to identify patterns and trends.
Key Findings:
- AI literacy research before generative AI was primarily focused on K-12 education, with approaches including adapting machine learning curricula, teaching AI ethics, and comprehensive curricula combining both.
- Since the rise of generative AI, there has been a significant increase in post-secondary AI literacy research, with a strong focus on teaching the effective use of generative AI tools.
- Three major perspectives on AI emerged: technical detail (understanding how AI works), tool (learning to use AI tools), and sociocultural (examining AI's broader implications).
- Three approaches to literacy were identified: functional (practical skills), critical (evaluative awareness), and indirectly beneficial (gaining other benefits like increased STEM interest).
- The number of studies with an AI tool perspective increased dramatically after 2023, surpassing both technical detail and sociocultural perspectives by 2024.
- Research gaps exist in combining AI tool perspectives with critical literacy perspectives, which is crucial for teaching responsible AI tool use.
Implications: The findings highlight the need for more specific terminology within AI literacy discourse rather than using the generic umbrella term. The framework developed in this study can help researchers and educators more clearly articulate their objectives and approaches when designing AI literacy interventions. The study also suggests a need for greater attention to promoting critical literacy surrounding AI tools, especially in post-secondary settings where there is still a lack of empirical research on AI literacy interventions.
Limitations: The review excluded AI literacy related to pre-service teacher education, adult education, and discipline-specific professional contexts like healthcare and business. The review did not evaluate the effectiveness of the learning interventions studied, as the diverse methodologies and educational contexts made direct comparisons inappropriate. Generative AI tools often have age restrictions that may limit the feasibility of K-12 AI literacy research specifically focused on these tools.
Future Directions: The researchers suggest exploring how to promote critical literacy surrounding AI tools, particularly in post-secondary settings, and conducting more empirical AI literacy research in higher education contexts. They also note the need to explore how combining technical AI knowledge with AI tool use might enhance students' ability to use AI effectively and responsibly.
Title and Authors: "AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review" by Xingjian (Lance) Gu and Barbara J. Ericson.
Published On: March 4, 2025
Published By: ArXiv