AI reading spine

The published basis for this course’s AI module

This page lists the sources that ground the course’s approach to generative AI literacy. It is meant to be student-readable: you can use it to follow up on the ideas behind the AI use guidelines, the AI Use Note standard, and the AI module work in Weeks 11–12 (the AI debugging audit and the AI Use Reflection).

The list is intentionally short — frameworks, peer-reviewed studies, and reputable institutional guidance. It is not a survey of news articles or hot takes.

How to use this page

For each source, you’ll find:

  • a citation so you can find the original,
  • a why it matters sentence,
  • a student-facing takeaway, and
  • the course activity or policy it informs.

If a link is to a publisher landing page or organization (e.g., UNESCO), search by title there for the current PDF — official documents are sometimes re-versioned. For peer-reviewed articles, search by title via Google Scholar or your library if a direct link is not provided here.

All sources below are cited as supplied by the instructor. Page numbers and DOIs are not included unless verified independently.

Frameworks

UNESCO — AI Competency Framework for Students (2024)

  • Citation. UNESCO. AI Competency Framework for Students. Paris: UNESCO, 2024.
  • Why it matters. A widely cited international framework that articulates what “AI literacy” looks like at the learner level: understanding, evaluating, and ethically using AI systems.
  • Student takeaway. AI literacy is not just being good at prompting. It is also understanding what these systems are, where they fail, and what your responsibilities are as a user.
  • Course activity / policy it informs. The four-pillar framing of “responsible use, verification, disclosure, critique”; the AI module (Weeks 11–12); the AI Use Note standard.

UNESCO — AI Competency Framework for Teachers (2024)

  • Citation. UNESCO. AI Competency Framework for Teachers. Paris: UNESCO, 2024.
  • Why it matters. The teacher-facing counterpart to the student framework. Establishes what an instructor should model and assess with respect to AI competency.
  • Student takeaway. The instructor side of this course is being built against a framework, not improvised. You can expect the course’s AI expectations to line up with a recognizable international standard.
  • Course activity / policy it informs. The course’s AI policy and the verification-centered framing of every AI Use Note.

Peer-reviewed and policy studies

Pepin, Buchholtz, & Salinas-Hernández — A scoping survey of ChatGPT in mathematics education (2025)

  • Citation. Pepin, B., Buchholtz, N., & Salinas-Hernández, U. “A Scoping Survey of ChatGPT in Mathematics Education.” Digital Experiences in Mathematics Education, 2025.
  • Why it matters. A peer-reviewed scoping survey of how generative AI has been studied and used specifically in mathematics education contexts.
  • Student takeaway. People are studying what works and what doesn’t when AI is used to learn math. The course’s framing of “AI as a study partner you must verify” sits inside an active research conversation.
  • Course activity / policy it informs. The mathematics-specific framing of the AI module; the emphasis on AI’s weakness at math correctness in the AI use guidelines.

Jin, Yan, Echeverria, Gašević, & Martinez-Maldonado — Generative AI in higher education: a global perspective of institutional adoption policies and guidelines (2025)

  • Citation. Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. “Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines.” Computers and Education: Artificial Intelligence, 2025.
  • Why it matters. A peer-reviewed cross-institutional study of how universities worldwide are writing policy around generative AI. Useful for understanding what the institutional landscape actually looks like.
  • Student takeaway. AI policy is not yet settled. Different schools and instructors land in different places. This course lands on the disclose-and-verify side, in line with most of the peer-reviewed literature.
  • Course activity / policy it informs. The course’s “disclose with an AI Use Note, verify in line with stated habits” stance.

Liang et al. — GPT detectors are biased against non-native English writers (Patterns, 2023)

  • Citation. Liang, W., et al. “GPT detectors are biased against non-native English writers.” Patterns (Cell Press), 2023.
  • Why it matters. A peer-reviewed study finding that automated AI-detection tools disproportionately misclassify writing by non-native English speakers as AI-generated.
  • Student takeaway. Automated AI detectors are unreliable and can cause real harm. They are not used in this course.
  • Course activity / policy it informs. The course’s “no detectors, no automated accusations” position. What matters instead: a clear AI Use Note, work you can explain, visible verification. (See “A note on AI detectors and accusations” in the AI use guidelines.)

Supplemental / preprint

Zhang & Magerko — Generative AI Literacy: a comprehensive framework for literacy and responsible use (2025)

  • Citation. Zhang, D., & Magerko, B. “Generative AI Literacy: A Comprehensive Framework for Literacy and Responsible Use.” 2025. Treat as preprint / supplemental.
  • Why it matters. A broader literacy framework that maps the cognitive and ethical dimensions of using generative AI well. Complements the UNESCO student framework with a more granular literacy structure.
  • Student takeaway. “AI literacy” has structure — there are separable skills (understanding the model, evaluating output, using ethically, critiquing). The course tries to give you experience with each.
  • Course activity / policy it informs. The framing of the AI module as four interlocking skills (use, verification, disclosure, critique) rather than a single “use AI well” objective.

What’s deliberately not on this list

  • News articles and op-eds. Useful context, but not stable enough to anchor a syllabus.
  • AI-vendor blog posts and marketing. Not independent.
  • Detector tool product pages. The course does not use them; see Liang et al. above.

If you come across a source that you think belongs here, send it to me — this page is updated each term as the literature evolves.

Reuse note

This page is part of the course’s public materials and is shared under the same terms as the rest of the public Quarto site. Citations above are to third-party works held by their respective publishers and authors; follow the publishers’ terms of use to access them.