Michael Rowe

January 2026

Notes

  • Boyer's model of scholarship30 January 2026A multidimensional framework for scholarship spanning discovery, integration, application, and teaching.
  • GraphRAG30 January 2026GraphRAG combines knowledge graphs with retrieval-augmented generation. Where ordinary RAG returns the passages most similar to a question, GraphRAG extracts the entities and relationships in a document collection and answers by following the connections between them, which is what questions about critiques, influences, or curriculum coverage need.
  • Knowledge graph30 January 2026How a knowledge graph differs from a vector database: it stores entities and the typed relationships between them, so AI can reason across connections.
  • Multi-hop reasoning30 January 2026Multi-hop reasoning with knowledge graphs: how AI answers questions that take several steps, and why recorded relationships make each step checkable.
  • AI literacy29 January 2026What is AI literacy? Six connected capabilities, from recognising AI systems to judging when they serve your goals, that have to develop together.
  • The common architecture of literacy29 January 2026The six dimensions of literacy that information, media, digital, data, and AI literacy frameworks share, from access and evaluation to contextual judgement, and why that means AI literacy builds on what educators already teach.
  • What a course can claim about AI literacy29 January 2026Any claim that a course or programme develops AI literacy needs qualifying: literacy develops through sustained practice, is developmental and contextual, and can't be fully assessed at the end of a course.
  • Prompt engineering9 January 2026Writing instructions for a language model in plain language, and refining them through conversation, until it produces what you need. The skill is closer to managing a colleague than to programming, which is why educators who supervise and give feedback already have most of it.
  • Context engineering8 January 2026How context engineering differs from prompt engineering: building the knowledge and structure a language model needs to reason well about your work.
  • Context sovereignty8 January 2026What is context sovereignty? The idea that people should own and author the context they bring to AI (their values, commitments, and ways of making sense of things) so that AI adapts to their thinking. How it differs from data sovereignty and model memory, its three principles, and what it means for students and educators.