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authentic-material-lesson-adapter

Turn authentic material (news articles, podcasts, videos, songs, menus, timetables, adverts, social media posts, forms, infographics) into language lessons by grading the task rather than the text. Selects texts for interest, relevance, exploitability, coverage, cultural load and rights; finds the 4-6 words to pre-teach, words to gloss on the page and words learners can ignore, plus names and cultural references to explain, with reading time by level; and builds a lesson shape (lead-in, pre-teach, gist or scanning, graded detail task, language focus on chunks and register, response) with task ideas per material type and level from A1 to C1. Covers copyright, fair dealing and fair use, Creative Commons and attribution. Use when a teacher wants to use a real-world text, video or song in class, or asks how to make authentic material work at a low level.

Languages, Linguistics & Translation / Language Teachingv1.0.0Updated OCT 25 20263 references1 script

Authentic texts motivate learners and show real language; they work at any level when the task is graded to the learners, not the text to the task. scripts/vocab_extract.py shows what to pre-teach, gloss or ignore, and whether the text can support detail tasks.

Trigger Conditions

Activate this skill when the user:

  • Wants a lesson from a real article, video, podcast, song, menu, ad or post
  • Asks how to use authentic material with low-level learners
  • Needs to choose what vocabulary to pre-teach from a real text
  • Asks whether they may copy or share a text, video or song in class

To rewrite a text for a level instead: reading-comprehension-text-adapter. Audio and video tasks: listening-task-builder.

Workflow

text
1. SELECT   -> interest, relevance, exploitability, length, cultural load, sensitivity, rights
2. ANALYSE  -> python scripts/vocab_extract.py text.txt --level B1 --task-words ...
3. TASK     -> grade the task to the level (references/exploiting-authentic-texts.md)
4. SHAPE    -> lead-in -> pre-teach (max 6) -> gist/scan -> detail -> language focus -> response
5. SUPPORT  -> gloss on the page; explain cultural references in the lead-in
6. CREDIT   -> source, date, licence; cut to the useful part (references/copyright-and-sources.md)

If coverage is under about 85%, keep to gist and scanning tasks, cut to a shorter extract, or adapt the text.

Output

PartContent
Text choiceWhy it suits the class; the extract to use; source and licence
VocabularyPre-teach items with how to teach them; glosses; words to ignore
BackgroundNames and cultural references with one-line explanations
TasksGraded gist/scan task, detail task, language focus, response, with answer key
PlanStage table with timings

Scripts

bash
python scripts/vocab_extract.py text.txt [--level B1] [--task-words word ...] [--max 6]

Ranks less frequent words by repetition, appearance in the title or first sentence, and task relevance; groups word forms. Exit codes: 0 printed, 2 read error or empty text.

Additional Resources

FileContent
exploiting-authentic-texts.mdGrade the task not the text, selection criteria, task ideas for ten material types, lesson shape, building tolerance of not understanding
copyright-and-sources.mdTeaching exceptions in the UK, US, EU, Canada and Australia; Creative Commons; safer sources; attribution; practical rules
high-frequency-words.txtAbout 1,000 high-frequency English base forms used by vocab_extract.py
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