## 2 Literature Review  
As the use of AI technologies in K-12 education expands, the educational implications of AI Literacy are becoming increasingly significant. However, AI Literacy is often conflated with digital literacy or understood as part of AI education, and its conceptual independence is not always clearly defined [17; 18; 19; 20; 21; 22]. This study aims to strengthen the foundation of our research by systematically discussing the definition of AI Literacy. To concretize AI Literacy, we first articulate it through a bottom-up approach based on the relationship between General Literacy and Literacy. General Literacy refers to the basic knowledge and competencies necessary for social participation, while Literacy involves the ability to understand and use information within specific contexts [23; 24; 25]. Traditionally focused on reading and writing, the concept of Literacy was expanded by The New London Group [26] through the introduction of 'Multiliteracies', emphasizing modes of meaning-making in digital and multicultural environments. [PERSON] [27] extended the scope of literacy within the information technology environment by introducing 'Digital Literacy'. AI Literacy extends Digital Literacy, encompassing the understanding of AI, its critical analysis, and ethical use [22; 28]. While some previous studies viewed AI Literacy as a subset of digital literacy [21], recent research suggests it is developing as an independent concept reflecting the unique characteristics and societal impacts of AI technology [22; 29]. Next, we compare the educational approaches of AI education and AI Literacy. AI education, as part of computing education (Depending on national educational policies and academic traditions, the names for computing curricula in K-12 education are interchangeably used as Informatics, Computing, ICT, etc. [30; 31; 32; 33; 34]. CC 2020 acknowledges these differences and clarifies that these terms are used interchangeably to mean the same thing. Initially, the concept of \"Introduction to AI\" (1991) was included in computer science education [35], and over time elements such as \"Machine Learning\", \"Data Mining\" (2001), \"Natural Language Processing\", and \"AI Ethics\" (2021) were added, gradually expanding the scope [35]. Recently, the \"AI Competency Framework\" was introduced, emphasizing that not only AI technical competencies but also ethical considerations must be addressed at all educational levels [35]), focuses on developing problem-solving skills through understanding AI concepts and principles, processing data, and creating models [30; 31; 32; 33; 34]. International K-12 AI education guidelines play a crucial role in establishing and developing an AI education system [20]. UNESCO [36] is developing a K-12 AI curriculum, setting directions for AI education in various countries, and AI4K12 recommends designing curricula around the five Big Ideas of \"Perception of Intelligence\", \"Representation and Reasoning\", \"Learning\", \"Natural Interaction\", and \"Societal Impact\" [14]. This highlights that AI education should encompass ethical and social responsibilities beyond mere technical acquisition [20]. This includes learning AI concepts, practical tech applications, and the integration of AI with other academic disciplines [37; 35; 20; 28; 36; 3]. In contrast, AI Literacy is not confined to a specific subject area but encompasses understanding the basic principles of AI technology, using tools, and analyzing its social and ethical impacts [37; 38; 22; 28; 3]. In AI-based educational environments, teachers are required to do more than merely use AI tools; they must effectively utilize AI technologies within educational contexts and critically assess their use [21; 29]. Teachers need to understand AI's basic concepts and operational principles to develop and apply appropriate instructional strategies that align with learning objectives.