# Analyzing the Alignment between AI Curriculum and AI Textbooks through Text Mining
## 2 Related Research
### Trends in AI Education Content
#### 2.1.1 AI-Related Knowledge Areas in Structuring Standard Curriculum  
(Higher Education)  
The artificial intelligence (AI) curriculum should be structured through a linkage between higher education curriculum standards and the K-12 curriculum. This approach is essential to reflect the continuously evolving AI technology, AI industry that embodies it, and potential impact on AI program development.  
Since the 1960s, the ACM/IEEE has supported global computing education by proposing higher education curriculum standards that align with the constantly evolving computing technology landscape. In 2020, the revision from CC 2005 to CC 2020 incorporated AI content within the field of computer science, specifically under the \"intelligent systems (IS)\" knowledge area (KA), one of the 18 KAs in the field [4; 5; 6]. In the data science field, AI content was included in three of the eleven KAs - \"AI\", \"data mining (DM)\", and \"machine learning (ML)\". Detailed information is provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
In computer science, AI has been extensively studied in the IS domain as a solution to challenging or impractical problems using traditional methods. AI finds widespread application in supporting everyday tasks, designing and analyzing autonomous agents that interact rationally with their environment. These solutions rely on diverse knowledge-representation schemes, problem-solving mechanisms, and learning techniques. They encompass areas such as sensing, problem solving, acting, and the architectures required to support these intelligent agents.  
AI encompasses methodologies that aim to model and simulate various human abilities recognized as aspects of intelligence within data science. The key themes of AI include perceiving, representing, learning, planning, and reasoning with knowledge and evidence. DM focuses on data processing, analysis, and presentation to extract valuable information. Clustering, classification, regression, pattern mining, prediction, association, and outlier detection are the essential types of analysis, with attention given to various types of data, including time series and web data. ML, also known as statistical learning, refers to a wide range of algorithms that identify patterns in data and use them to build prediction models. Data privacy, security, integrity, and analysis for security (DPSIA) are cross-cutting topics that apply to competencies across all KAs, particularly those that address privacy, security, and integrity concerns.  
AI content within the field of computer science is structured using the \"knowledge area, knowledge unit, and learning outcome\" (KA-KU-LO) model, with each knowledge unit associated with a list of topics. The learning outcomes are based on [PERSON]'s taxonomy and modified to include three levels of mastery: familiarity, usage, and assessment. In the field of data science, AI content is structured around a competency framework that includes knowledge, skills, and dispositions (K-S-D). Skills are expressed through learning outcomes, utilizing [PERSON]'s level of cognitive processes to specify the level of skill required for successful task completion. Descriptive learning outcomes aid in the clarification of intended goals and facilitate the interpretation of outcomes within a specific curriculum context.