2024 AI Literacies book chapter drafting

What are the implications of “automnous” framings of literacy in AI?

How is “literacy” getting used in AI ed research?

If we want to understand how the paradigm of K12 AI education is taking shape, one place to look is at the language adopted by funding agencies. Following the explosion of popular excitement around ChatGPT, the National Science Foundation issued two “Dear Colleague Letters” (a genre adopted from Congress, where such letters are used to stake positions and build legislative coalitions [@box-steffensmeier2019]) on K12 AI education. Authors of grant proposals tend to read these letters carefully, hoping that proposals which are well-aligned with the NSF’s understanding of a field are more likely to be funded.

The first, from May 2023, focused on the use of AI in formal and informal K12 settings. This letter noted that AI is “rapidly transforming formal and informal educational settings and systems” and that “the nature of learning, teaching, and assessment is rapidly evolving.” The letter calls for research in several areas, including how AI can be used for teaching and learning, what children should learn about AI, and Ai’s impact on educational equity. The letter operationalizes these concerns by identifying four areas

  • Developing AI tools and environments to advance age-appropriate equitable learning and inclusive teaching;
  • Supporting learning about and interest in AI;
  • Using AI to teach AI; and,
  • Integrating generative AI in education in an ethical, responsible, and effective way.

Often the framing is ok, but the operationalization doesn’t follow through. This is understandable–it’s much easier to something simple than something complex–and it’s a long-term pattern in education. (So why is literacy used? What’s attractive about it?) The most immediate and relevant example of this is in the framing of computational thinking, the central concept in the framing of K12 computer science education. In its first articulation … trees upside down… literacy as competencies… A broader example of the same phenomenon: @eisenhart1996, reflecting on the design and implementation of the Next Generation Science Standards, argues that the initiative was originally focused on a broad understanding of scientific literacy, but that the assessments (which drive instruction in practice), ultimately focused on a narrow, autonomous framing of science.

  • PCAST

What are the language models doing? AI participates much more in the interpretation of the text, the enactment of language ideologies. We can imagine texts that are “self-reading.”

Also, we have evidence of a techno-language ideology, that people defer to the authority of computer models. (e.g. sleep trackers).

What does this mean four our thinking about equity?

References