# Analyzing the Alignment between AI Curriculum and AI Textbooks through Text Mining
## 2 Related Research
### Trends in AI Education Content
#### 2.1.2 K-12 AI Curriculum Content  
K-12 AI content can be categorized based on international standards and individual country curricula. International standards, such as the \"K-12 AI curricula\" developed by UNESCO (2022) and \"K-12 AI guidelines based on five big ideas\" promoted by AI4K12 (a collaboration between AAAI and CSTA), provide frameworks for K-12 AI education [3,7]. At the national level, various AI-related subjects, such as \"AI\" in the Indian CBSE school system (India's CBSE curriculum is divided into academic and skill subjects. \"Artificial Intelligence\" was first organized as a skill subject in the 2019-2020 curriculum and has been revised five times in the \"2020-2021\", \"2021-2022\", \"2022-2023\", and \"2023-2024\" curricula. This study targets senior secondary levels XI & XII in 2023-2024), \"Preliminary AI\" (\") in China (The high school IT curriculum in China consists of 10 modules, including two required modules, six elective required modules, and two elective modules), and \"Introduction to AI\" in Korea (In China, India, and Korea, AI curricula and textbook development projects are led by various IT companies, public institutions, and regional initiatives) [8, 9, 10], have been incorporated into high school curricula. Different approaches to competency are reflected in documents addressing K-12 AI educational content. UNESCO's K-12 and India's AI curricula consider \"competency\" from a competency-based framework perspective, as outlined in the CCDS 2021. Conversely, Korea's \"Introduction to AI\" includes competencies such as \"knowledge of information culture\", \"computational thinking skills\", \"cooperative problem-solving skills\", and \"basic knowledge of AI\". In this study, \"competency\" is defined as the ability to accomplish a specific task, and the components fostering competency within the competency-based framework include \"knowledge\", \"skills\", \"attitude\", and \"values\". K-12 AI international standards aim to provide a framework to guide standard writers and curriculum developers to understand AI concepts, essential knowledge, and skills at the grade level. The composition of K-12 AI international standards is presented in Table 2.  
The analysis of the K-12 AI curricula and K-12 AI guidelines yields the following results:  
The first aspect pertains to the composition of curricula. The K-12 AI curricula are structured based on competency-based education, with specific competencies (\"knowledge\", \"skills\", \"values\", and \"attitudes\") that students must achieve. In other words, the curricula categorize students' expected capabilities at each grade level based on their knowledge, skills, values, and attitudes in AI. \begin{table}
\begin{tabular}{c c c c} \hline \hline
In contrast, the K-12 AI guidelines are organized around five big ideas (1: perception, 2: representation and reasoning, 3: learning, 4: natural interaction, and 5: societal impact.) However, these guidelines do not include specific competency items as framework components. Instead, students are encouraged to develop comprehensive competencies related to the five big ideas by achieving learning objectives (LOs) and enduring understanding (EU). The second aspect relates to the expression of knowledge. The K-12 AI curricula address the value dimension by incorporating the four categories (\"personal\", \"social\", \" societal\", and \"human\") proposed by the OECD (2019), expanding the perspectives that students should consider. In the context of AI, curricula include aspects, such as personal goals, interpersonal relationships, shared priorities within cultures or societies (which may be legally established), and shared priorities, that transcend national and cultural borders. The names of knowledge and skill areas and domains include various perspectives. However, when attempting to reconstitute knowledge based on the K-12 AI curricula, the nomenclature when mixing types of knowledge, concepts, and competencies may pose limitations.