Together with colleagues from computer science, agriculture, food science, biology, and related fields, I am currently working on a framework for teaching artificial intelligence (AI) and machine learning (ML) to students and practitioners with limited computing skills. Assuming we are not the first ones to try to do this, I was looking for background research on this topic, with limited success. While there are plenty of entry-level tutorials and introductions to AI and ML, they are either at a highly conceptual level or intended for people with programming skills.

The closest publication is this article, aimed at educating creative practitioners with limited programming skills. The author gives an excellent overview of her approach and provides background research on ML education, among other topics. Also, with very limited success in her quest for solid research on this topic, she expands on an initial version of pedagogical content knowledge (PCK) for machine learning [1]: Why should students learn ML? What should they learn? How do students with limited programming and math skills learn ML concepts so they can use them in their domain? How do pedagogical techniques and the technology used influence student understanding of concepts and methods?

While there are some differences in the target audience, it was very helpful to see an example of learning objectives, strategies for effective teaching, and the use of technology for participants with limited programming and math skills. In addition, the author and her collaborators make some practical resources available to others through open-source arrangements. Now all we need is a good dose of transfer learning, a few nicely curated datasets, and some domain knowledge to apply this to our application area.