The main goal of the study was to review empirical research on AI teaching and learning in K-12 education from 2019 to 2022, focusing on learning outcomes and pedagogical approaches.

 

Methods: The research was conducted as a systematic literature review, analyzing 28 studies selected from 8,175 papers across five research databases. The studies were evaluated using content analysis to synthesize data on learners' contexts, pedagogical approaches, and theoretical coverage of AI topics.

 

Key Findings:

• Most studies reported improvements in both cognitive and affective learning outcomes.
• A learner-centered approach and context-aware pedagogical practices were suggested to be beneficial.
• There is a lack of consistent constructs to measure AI learning outcomes, and more empirical support is needed for pedagogical approaches.

Implications: The findings contribute to the field of AI in education by highlighting the potential benefits of AI literacy and the need for a more structured approach to teaching AI in K-12 settings. The study emphasizes the importance of developing consistent measures for AI learning outcomes and integrating AI education into the curriculum.

 

Limitations: The review was limited by the small number of empirical studies available and the short time span of the research. The findings may not be generalizable beyond the specific contexts studied.

 

Future Directions: Future research should focus on developing and validating methods to effectively measure AI learning outcomes and explore the integration of AI literacy into diverse educational settings. There is also a need for longitudinal studies to assess the long-term impact of AI education.

 

Title and Authors: "Artificial Intelligence Teaching and Learning in K-12 from 2019 to 2022: A Systematic Literature Review" by Saman Rizvi, Jane Waite, and Sue Sentance.

 

Published On: 2023

 

Published By: Computers and Education: Artificial Intelligence