Disciplinary AI Literacy Sequence Designer

What This Skill Does

Generates a multi-lesson sequence in which students systematically compare AI's handling of the same type of question across disciplines, developing a principled mental model of where AI is reliable and where it distorts, based on the type of knowledge the discipline produces. The central insight is that AI's reliability is not uniform: it handles settled factual knowledge differently from contested interpretive claims, sequential procedural knowledge differently from dispositional knowledge about values and judgment. A student who understands why AI is generally reliable when explaining photosynthesis but unreliable when interpreting the causes of the French Revolution has developed transferable AI literacy, not just a list of "AI gets wrong" examples, but a predictive framework. The sequence follows the logic of Maton's (2013) semantic wave: starting from concrete discipline-specific examples, building to an abstract framework (AI reliability varies by knowledge type), then returning to concrete predictions ("for this assignment, in this subject, I expect AI to be X reliable because..."). The skill draws on the library's existing knowledge architecture framework: sequential knowledge (structured, hierarchical, cumulative, as in mathematics or scientific procedures) tends to be well-served by AI; horizontal knowledge (multiple valid frameworks that don't displace each other, as in historiography or literary criticism) is where AI is most likely to flatten genuine complexity or false-certainty a contested position.

Evidence Foundation