Make training materials accessible and inclusive
Audits slides, e-learning, documents, and videos for accessibility barriers and non-inclusive content, with specific fixes.
The prompt
Review my training materials for accessibility and inclusion.
Materials: [PASTE TEXT, SLIDE CONTENT, SCRIPT, OR DESCRIBE THE MODULE]
Format: [slides / e-learning / PDF / video / live virtual session]
Learners: [WHO, including known needs - e.g. "global, many non-native English speakers", "frontline staff on mobile"]
Standard we follow: [e.g. WCAG 2.2 AA, internal policy, or "not sure"]
Check for:
- Visual: color contrast, color used as the only signal, text in images, font size, alt text for meaningful images, slide reading order.
- Audio and video: captions, transcripts, audio description for key visuals, no auto-play.
- Interaction: keyboard navigation, drag-and-drop with no alternative, timed activities, flashing content.
- Cognitive load: reading level, jargon and acronyms, dense screens, unclear instructions, too many steps at once.
- Inclusion: examples, names, and images that reflect a range of people; stereotypes; idioms and culture-specific references that confuse global learners; gendered or ableist language.
- Live sessions: multiple ways to participate (chat, voice, polls), materials shared in advance.
Return:
1. A table: Issue | Where | Who it affects | Fix | Priority (must / should / nice).
2. Rewritten versions of the 5 most problematic passages.
3. Alt text for any images I described.
4. A short checklist I can reuse on future courses.
Note anything you cannot check from text alone (e.g. actual contrast ratios, screen reader behavior) and tell me how to test it.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Learning & Teaching prompts
What to fill in
- PASTE TEXT, SLIDE CONTENT, SCRIPT, OR DESCRIBE THE MODULE
- slides / e-learning / PDF / video / live virtual session
- e.g. WCAG 2.2 AA, internal policy, or "not sure"
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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