Manuscript received April 10, 2023; revised June 1, 2023; accepted July 5, 2023.
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.
Index Terms—Artificial intelligence, constructionism, image
recognition, machine learning, neural networks, syntonic
learning
Mark W. Barnett and Arnan Sipitakiat are with Chiang Mai University,
Chiang Mai, Thailand.
Paulo Blikstein is with the Teachers College, Columbia University, New
York, USA.
*Correspondence: markwilliam_barnett@cmu.ac.th (M.W.B.)
Cite: Mark W. Barnett*, Arnan Sipitakiat, and Paulo Blikstein, "Neural Syntonicity: A Constructionist Approach to the Development of Image Recognition Tools Used to Teach Students about Powerful Ideas in Artificial Intelligence," International Journal of Information and Education Technology vol. 13, no. 12, pp. 1917-1923, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).