Art educators are increasingly concerned about the potential "deskilling" effects of text-to-image
technologies in K-12 art classrooms. That is, the degree to which over-reliance on image
generators might diminish tactile and motor skills gained through the mastery of
age-old
artistic methods such as:
brush painting with watercolors,
gradient shading with colored pencils,
molding shapes with clay & more.
The "AI Toolkit for Art Educators" grew out of the desire to explore how
generative tools might reshape, and entangle artistic processes in K-12 settings, and
the extent to which such processes might center
the artistic needs and desires of Black and Brown middle school students in New York City urban schools.
We draw upon critical perspectives from Black and Brown Artists
and Art
Educators who are united by
the need to advance art educational equity. Their lived experiences, which spans utilizing, resisting
and
experiencing the harms of generative AI, have led us to prioritize material, logistical and
on-the-ground pedagogical
questions, rather than abstract, conceptual theories.
This toolkit is a living document, which will change in response to developments in the field. To identify the most recent update, please look for the "last update" button at the bottom right of the page.
The AI Toolkit for Art Educators is a project led by Minne Atairu. It is made possible by grants from the The Processing Foundation, and Columbia University's Center for Science and Society. Reviews and contributions for this toolkit were provided by: Aarati Akkapeddi, Innanoshe Akuson, Harshit Agrawal, Jacqueline Coefield, Jurnee Coker, Curry Hacket, Olga Hubard, Fabiola Larios, Moises Sanabria, Kandice Stewart, Lisett Llovera, Layla Quinones.
This toolkit is licensed under a Creative Commons Attribution 4.0 International License. You are free to share, copy, distribute, and transmit the work, as well as to adapt it, provided that you attribute the work to the original author(s) and source, do not use it for commercial purposes, and share any derivative works under the same or a similar license.