Published in KI - Künstliche Intelligenz, German Journal of Artificial Intelligence 2022
Introductory article in the Special Issue on Learning Computational Thinking.
Authors: Nina Bonderup Dohn, Yasmin Kafai, Anders Mørch og Marco Ragni
Learning is central to both artificial intelligence (machine learning) and human intelligence (human learning). This survey examines the connections between the two, and points to the need for educating the general public, to understand the challenges that the increasing integration of AI in human lives pose.
The framing of Computational Thinking (hereafter CT) as: cognitive, situated and critical, provides valuable insights into what CT can and should be. The differences between the three framings also concern the views of learning that they embody. The authors combine the three framings into one, which emphasizes that
(1) computational thinking activities involve engagement with algorithmic processes, and
(2) the mere use of a digital artifact for an activity is not sufficient to count as computational thinking.
And further present a set of approaches to learning CT.
They argue for the significance of CT as regards artificial intelligence on three counts:
(i) Human developers use CT to create and develop artificial intelligence systems,
(ii) understanding how humans learn can enrich artificial intelligence systems, and
(iii) such enriched systems will be explainable.
The authors conclude with an introduction of the articles included in the Special Issue, focusing on how they call upon and develop the themes of this survey.
Dohn, N.B., Kafai, Y., Mørch, A. et al. Survey: Artificial Intelligence, Computational Thinking and Learning. Künstl Intell (2022). https://doi.org/10.1007/s13218-021-00751-5