Analyzing the Alignment between AI Curriculum and AI Textbooks through Text Mining

Hyeji Yang, Jamee Kim, Wongyu Lee

Research output: Contribution to journalArticlepeer-review

Abstract

The field of artificial intelligence (AI) is permeating education worldwide, reflecting societal changes driven by advancements in computing technology and the data revolution. Herein, we analyze the alignment between core AI educational curricula and textbooks to provide guidance on structuring AI knowledge. Text mining techniques using Python 3.10.3 and frame-based content analysis tailored to the computing field are employed to examine a substantial amount of text data within educational curriculum textbooks. We comprehensively examine the frequency of knowledge incorporated in AI curricula, topic structure, and practical tool utilization. The degree to which keywords are reflected in curriculum textbooks and in the textbook characteristics are determined using Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) analysis, respectively. The topic structure distribution is derived by Latent Dirichlet Allocation (LDA) topic modeling and the trained model is visualized using PyLDAvis. Furthermore, the variation in vertical content range or level is investigated by content analysis, considering the tools used to teach similar AI knowledge. Lastly, the implications for AI curriculum structure are discussed in terms of curriculum composition, knowledge construction, practical application, and curriculum utilization. This study provides practical guidance for structuring curricula that effectively foster AI competency based on a systematic research methodology.

Original languageEnglish
Article number10011
JournalApplied Sciences (Switzerland)
Volume13
Issue number18
DOIs
Publication statusPublished - 2023 Sept

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • Latent Dirichlet Allocation (LDA)
  • Term Frequency-Inverse Document Frequency (TF-IDF)
  • artificial intelligence (AI) curriculum
  • artificial intelligence (AI) education
  • content analysis
  • text mining

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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