The main goal of this study was to synthesize empirical studies on K-12 AI education to understand how AI is taught at this level and to inform future AI curriculum development.
Methods: The study conducted a scoping review of 21 articles, analyzing AI curriculum design and learning effects in K-12 education. The literature search involved querying three electronic databases and applying exclusion criteria to refine the results.
Key Findings:
- Most studies aimed to teach students basic AI knowledge, covering topics like machine learning and deep learning.
- Various teaching methods, such as inquiry-based, project-based, and game-based learning, were used.
- Participation in AI curricula enabled students to understand AI functions, apply AI knowledge, evaluate and create AI applications, and grasp AI-related ethical issues.
Implications: The findings contribute to the field of AI in education by highlighting effective teaching methods and curriculum designs that can enhance AI literacy among K-12 students. This supports the integration of AI education into early learning to prepare students for an AI-driven future.
Limitations: The study's limitations include a small number of reviewed articles, a focus on English-language studies, and a lack of consideration for social and economic factors affecting AI curriculum application.
Future Directions: Future research should explore the implementation of AI curricula in developing countries, develop standardized assessment tools for AI knowledge, and focus on the learning effects of using and applying AI, evaluating and creating AI, and understanding AI ethics.
Title and Authors: "Teaching artificial intelligence in K-12 classrooms: a scoping review" by Jiahong Su, Kai Guo, Xinyu Chen, and Samuel Kai Wah Chu.
Published On: May 24, 2023
Published By: Interactive Learning Environments, Routledge Taylor & Francis Group