OER·harvester

Competency catalogue

The 104 active T2.3 competencies: the vocabulary the harvester searches with, the descriptor the alignment reads, and what the alignment found. Compose a harvest →

What the alignment foundHow often each was searched

How many resources the alignment reads as contributing to each competency or developing it: decided at level 2 or 3, or not decided yet and more likely than not to reach 2. 54 competencies have none, 24 one or two. Every figure is a lower bound: on a sample graded on every competency, the map counts 33% of what was graded 2 or 3, and almost nothing of the 5 competencies underlined with dots.

How many times a harvest has searched for each competency, once for each source it asked. 101 competencies have never been searched. A search is not material found: the aligned view says what came of it.

none 1–2 3–9 10–29 30+
never 1–2 3–5 6–11 12+
DDigital 94212203420012149086012303134 GGreen 0000000102000010000100010 RResilience & Entrepreneurial 004000000000000010000120000 TDeep Tech 72643202372825212335201140010

D08 Digital · Knowledge · Intermediate

Generative AI Literacy

Understand capabilities and limitations of generative AI models

Alignment

42 resources likely to contribute to it or develop it

4 decided at level 2 or 3 (1 develops it) · 38 not decided yet, more likely than not

On a graded sample, 11 pairs were graded 2 or 3 for it, and the map counts 3.

Retrieval

0 resources harvested by searching its terms

Never searched yet.

Search vocabulary

Domain group
generative AI literacygenerative model capabilitiesgenerative model limitationsfoundation modelslarge language model capabilitieslarge language model limitationstext generationimage generationmultimodal generation

Zenodo and arXiv take the whole group in one query. GitHub takes one phrase at a time and works left to right, stopping once the competency is satisfied — so on that lane this order decides which terms actually run.

The compiled queries
zenodo ("artificial intelligence" OR "machine learning" OR "generative AI" OR "deep learning" OR "reinforcement learning" OR "large language model" OR "AI") AND ("generative AI literacy" OR "generative model capabilities" OR "generative model limitations" OR "foundation models" OR "large language model capabilities" OR "large language model limitations" OR "text generation" OR "image generation" OR "multimodal generation")
arxiv (all:"artificial intelligence" OR all:"machine learning" OR all:"generative AI" OR all:"deep learning" OR all:"reinforcement learning" OR all:"large language model") AND (all:"generative AI literacy" OR all:"generative model capabilities" OR all:"generative model limitations" OR all:"foundation models" OR all:"large language model capabilities" OR all:"large language model limitations" OR all:"text generation" OR all:"image generation" OR all:"multimodal generation")
github · term 1 AI "generative AI literacy" in:name,description,readme fork:false archived:false
github · term 2 AI "generative model capabilities" in:name,description,readme fork:false archived:false
github · term 3 AI "generative model limitations" in:name,description,readme fork:false archived:false
github · term 4 AI "foundation models" in:name,description,readme fork:false archived:false
github · term 5 AI "large language model capabilities" in:name,description,readme fork:false archived:false
github · term 6 AI "large language model limitations" in:name,description,readme fork:false archived:false
github · term 7 AI "text generation" in:name,description,readme fork:false archived:false
github · term 8 AI "image generation" in:name,description,readme fork:false archived:false
github · term 9 AI "multimodal generation" in:name,description,readme fork:false archived:false

Profile fda2d44e5826475c

Alignment descriptor draft

Outcomes
  • Can explain what generative models produce and, broadly, how.
  • Can describe their limitations: hallucination, bias, knowledge cut-off, cost.
  • Can choose sensible uses for generative AI and recognise poor ones.
Develops it

An introduction to generative AI for users: what large language and image models do, what they cannot do, and examples that show both.

Only mentions it

A lab that calls a generative model through an API to build an application, with nothing on its capabilities or limits.

Alignment terms
generative ailarge language modelsllm limitationsknowledge cutoffcontext windowtext-to-image

Read in a resource's text beside the domain group. They are never compiled into a search.

Neighbours

Descriptor 0592f985c7f2

How a vocabulary becomes a search

Every candidate must match both concept groups in its title, description or keywords before any content is downloaded. Precision warnings are recorded for audit; they never silently cancel positive evidence.

  1. Official name and description
  2. AI/ML terms and domain terms
  3. Zenodo, arXiv and GitHub syntax
  4. Metadata relevance gate
Mandatory AI/ML group
artificial intelligencemachine learninggenerative AIdeep learningreinforcement learninglarge language modelAI

GitHub's repository search takes one phrase at a time. Measured against the live endpoint, a parenthesised OR group returns nothing at all where each term alone returns thousands, and the 256-character limit rejects a long group outright — so that lane issues one query per term and stops as soon as the competency is satisfied.

coverage-vocabulary-1.3.0+sha256:08ad2cd19868 · catalogue sha256:04970bfc77255b1f

What a descriptor is

A descriptor is how the alignment reads a competency: what a learner who has it can do, what a resource has to do to develop it, what only mentions it, the phrases its subject is written in, its false friends, and how to tell it from the competencies that share its words. The definition above it stays the authority.

Every descriptor is a draft until a partner approves it; 0 of 104 are approved. Read the alignment rubric →

competency-descriptors-1.0.0+sha256:3cc741a61ced