HCAI Block Model: A competence model for Human Centred Artificial Intelligence at K-12

Brian Conway, Keith Nolan, Keith Quille

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Artificial Intelligence (AI) is becoming a common topic within the
computing K-12 curricula worldwide. While much of the focus
of research is on the use of Generative AI in and for education,
AI as a core subject area is still gaining popularity, with much of
this research focusing on content and tools that effectively support the teaching of AI. However, as we grow as a field, there is
a need currently unmet to provide foundations (in the form of a
block model as there exists for programming) to allow researchers
to build strong pedagogies and methodologies from, and even a
base to design activities and content. Compounding this, as ethics
and its relationship to AI in the K-12 classroom grows stronger,
there is a further need to provide scaffolding to educators and researchers not only on traditional AI concepts, but also on how they
link with ethical knowledge, skills and dispositions. In this paper,
the Human Centered Artificial Intelligence (HCAI) Block Model is
developed and introduced. This is a competence-based model to
guide effective teaching and learning of Human Centered Artificial
Intelligence, as well as research in the K-12 space. The HCAI Block
model’s foundation is developed/adapted from the programming
Block model and has been adapted and developed using two lenses.
The first was through the data science lens through interaction
with Computational Thinking 2.0 and competency-based learning.
The second lens was through a human-centred lens. The outcome
was a ground-up K-12 model where traditional and technical AI
concepts have been developed from the start, integrating ethical
considerations and human-centred approaches.
Original languageEnglish
Title of host publicationHCAI-ep 2024 - Proceedings of the 2024 Conference on Human Centered Artificial Intelligence - Education and Practice
Pages22-28
Number of pages7
ISBN (Electronic)9798400711596
DOIs
Publication statusPublished - 2 Dec 2024

Publication series

NameACM International Conference Proceeding Series

Keywords

  • Block Model
  • Computational Thinking 2.0
  • Computing Education
  • Ethics
  • Human-Centered AI
  • Machine Learning

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