Neural Syntonicity: A Constructionist Approach to the Development of Image Recognition Tools Used to Teach Students about Powerful Ideas in Artificial Intelligence

Abstract

Although Artificial Intelligence (AI) is already being used in a variety of ways to support creativity and education, there are still limitations when it comes to understanding how AI becomes intelligent, its impacts and how to manipulate, tinker with and explore future uses. This work builds on the idea of “syntonicity” as a cognitive tool where learners benefit from their existing understanding of intelligence while learning about AI. This work presents a learning framework called “Neural Syntonicity” which describes the syntonic relationship between the student’s thoughts and reflections while learning how to use and train AI Image Recognition tools. In this project we: 1) developed a series of Machine Learning Image Recognition software tools that students can manipulate and tinker with, 2) developed a “microworld” of activities and learning materials that supports a conducive learning environment for students to learn about Image Recognition, and 3) developed scenarios that allow students to explore their own cognitive labels of visual Image Recognition while using these tools. The research also aims to help students uncover “Powerful Ideas” and learn technical knowledge in Artificial Intelligence like: prediction, data clustering, accuracy, data bias, training and societal impacts. Using a mixed methods approach of Design Based Research, we conducted studies with three different groups of students. Through the analysis, we found that all groups of students gained confidence with using AI, and learned new technical skills in AI. Students were also able to demonstrate through a variety of examples that bias is a factor that can be controlled in AI systems as well as in the human mind.