Generative AI Literacy is a subfield of AI Literacy that deals with comprehension and competent use of generative AI technology. It is a specialized field in the broad realm of artificial intelligence. However, because of its peculiar applications and associated ethical implications, it is also considered separate and hence requires specially targeted education and awareness programs.

Hello Zhao Di Li

In my opinion,

Generative AI literacy is definitely a subset of AI literacy, but it’s kind of its own beast too. AI literacy covers the broad fundamentals—how AI works, ethical considerations, bias, automation, etc. Generative AI literacy, on the other hand, is more specific to tools like ChatGPT, Midjourney, or LLMs in general—how to prompt them effectively, understand their limitations, recognize AI-generated content, and evaluate outputs critically.

In higher education, the main difference comes down to usage and critical thinking. AI literacy is more theoretical and foundational—how AI models function, data ethics, and their societal impact. Generative AI literacy is hands-on—how students can use AI to enhance learning, research, and creativity while avoiding over-reliance or misinformation. It’s the difference between understanding what AI is and actually using it effectively.

As for standardized assessment tools… not really. Some institutions are developing rubrics, and there are frameworks floating around, but no universal standard has been established yet. It’s still a moving target, especially as AI keeps evolving. Most assessments right now are more about evaluating critical thinking and ethical usage rather than just technical knowledge.

Generative AI Literacy can be considered a subset of AI Literacy, as it focuses specifically on understanding, using, and critically evaluating generative AI technologies (e.g., text, image, or code generation), while AI Literacy encompasses a broader understanding of artificial intelligence concepts, applications, and implications. In higher education, the main differences lie in their focus: AI Literacy addresses foundational AI principles, ethics, and societal impacts, whereas Generative AI Literacy emphasizes skills like prompt engineering, output evaluation, and creative or ethical use of generative tools. While standardized assessment tools for AI Literacy are emerging, such as the AI Literacy Scale, tools specifically for Generative AI Literacy are still in development, with institutions often creating context-specific evaluations to measure students' proficiency in using and understanding generative AI systems.

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