Artificial Intelligence (AI) is the new literacy garnering the most attention in present education circles, with the primary focus being on what AI looks like in the classroom. Yim (2024) looks to answer this question by providing a conceptual framework for educators on implementation of AI in primary school classrooms. Yim analyzed 17 various studies that identified AI frameworks across a variety of levels. The article acknowledges that AI frameworks have been prevalent in the secondary to higher education levels, while sparingly being identified in the primary school levels. According to Yim, it is because “young students lack foundational knowledge regarding AI’s core technologies,” (2024). Yim’s purpose for the study relies on the emergence of AI tools specifically designed for primary school education, lacking the framework for educators to effectively implement them. The author proposes a conceptual framework that allows for more inclusiveness and ‘post-humanistic’ thinking as compared to conventional computational thinking (see the image below). 

The article uses a PRISMA method of systematic review following three specific steps: article selection, article screening, and data/article analysis. By identifying using key words related to the research questions throughout a variety of databases. From there, the author uses specific criteria to exclude articles based on title, non-education focuses and duplicates. Using this method, Yim identifies 19 total articles, 17 specifically focusing on AI conceptual framework. The criteria used to identify the sources included in this review were appropriate and allowed for the author to properly identify articles relevant to AI framework in educational settings.

One of the major strengths of this article is in its organization and explicit nature of their writing. When answering the outlined research questions, they are clearly identifiable and clearly answered within the article. Yim also excels in the ability to express the variety of methodologies used across the selected studies. By identifying the underlying methodologies, it allows for the reader to evaluate whether the provided frameworks looked at in this study are appropriate for their learning environment. Further, the study’s synthesizing of the articles to identify trends in the literature is effective. The tables included to represent the information are clear and present the findings within the various articles used in this review. These choices in transparency and clarity are helpful for readers to synthesize information and trends that are relevant to them.

Perhaps one of my favorite articles I have annotated this year, Yim (2024) was exceptional in its visual representation of its findings. That said, the application of the conceptual framework is appropriate for the level of my current career and the focus of my research long term. AI is becoming more prevalent in classrooms of all ages, K-12 and higher-education, and it cannot be ignored. When considering teacher preparation of educational technology resources, there is a resistance of a large number of educators who fear what AI can do and what issues it may cause in the classroom. It largely comes from ignorance from educators, of which is no fault of their own. Considering the work of Leander and Burriss (2020), the post-human world is evolving with AI leading the forefront and teacher preparation programs should reflect that. I am looking forward to investigating and researching further the impact AI framework has on teachers and their ability to implement AI in the classroom.

Sources
Leander, K. M., & Burriss, S. K. (2020). Critical literacy for a posthuman world: When people read, and become, with machines. British Journal of Educational Technology, 51(4), 1262-1276.

Yim, I. H. Y. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 100319.