AI Prompts for ElevenLabs Prompts for eLearning Narration

Cast the narration voice for my course: [TOPIC, AUDIENCE, SKILL LEVEL]. Teaching style: [STYLE: FRIENDLY COACH / EXPERT LECTURER / PEER WALKING ALONGSIDE]. Define the voice spec: register, pace for information density, warmth versus authority balance for my audience, and energy level that sustains without exhausting. List the audition criteria and what disqualifies a voice for long-form teaching.

Learners process while listening, so pace is pedagogy. Write a 90-second audition passage from my course content ([PASTE A DENSE SECTION]) and define the pace test: can a first-time learner follow at 1x, does the voice leave room after key concepts, do numbered steps land distinctly? Include the ElevenLabs settings adjustments if a good voice reads too fast for teaching.

My course has [NUMBER] modules produced over [TIMEFRAME]. Build the consistency system: the locked voice settings record, the script formatting standards every module follows, the test paragraph I regenerate before each production session to verify the voice still matches, and the fix protocol if ElevenLabs model updates shift the sound mid-course.

I am building [NUMBER] courses under one brand: [LIST TOPICS]. Decide the voice architecture: one voice for all (strongest brand identity), or voice-per-level or per-track ([STRUCTURE]). Weigh recognition, learner expectations per topic, and production simplicity. Recommend the architecture and write the voice spec for each slot in it.

Audit my narration plan against accessibility needs: clarity at slower playback speeds, compatibility with captions and transcripts (which I generate from scripts), pronunciation consistency for screen-reader-familiar learners, and pace options. List what to build into production from day one so the course serves learners with [NEEDS: HEARING, COGNITIVE, ESL] rather than retrofitting later.

Convert my lesson content into a narration script: [PASTE SLIDES / NOTES / DRAFT]. Apply learning-audio rules: signpost structure out loud (first, next, the key point here), one idea per sentence, define terms at first use, repeat the core concept in different words at section end, and convert visual references (as you can see) into spoken descriptions or explicit slide cues. Format for ElevenLabs delivery.

Mark up my script for cognitive load: [PASTE SCRIPT]. Identify where information density peaks and insert breathing room: a pause after each new concept (paragraph break), a summary sentence after dense sequences, and an example after every abstraction. The goal: a learner taking notes never rewinds because the narration outran them. Output the reformatted script.

My lessons average [LENGTH] minutes and completion drops at [POINT]. Build the engagement rhythm into my scripts: a direct question to the learner every [INTERVAL], a concrete example or mini-story per concept, second-person framing throughout (you will, your project), and an energy reset phrase at section transitions. Apply the rhythm to this script: [PASTE].

My course teaches [TECHNICAL FIELD] with terms ElevenLabs may garble: [LIST TERMS, ACRONYMS, CODE, FORMULAS]. Build the pronunciation sheet: phonetic respellings for the script, the spoken form of each acronym (letter by letter or as a word), how to read code and symbols aloud clearly, and the consistency rule so every module says the term identically.

Script the interactive moments in my course: question introductions that do not give the answer away by tone, the pause instruction before answers (and how to create that space in the audio), answer explanations for right and wrong choices, and exercise walkthrough narration for [ACTIVITY TYPE]. Write the templates plus one worked example from my content: [PASTE A QUIZ ITEM].

Design my course audio pipeline: script finalization, pronunciation-sheet check, generation in ElevenLabs (per-lesson files, [NAMING: COURSE_MODULE_LESSON]), QA listen, sync with [PLATFORM / SLIDES / SCREEN RECORDINGS], and publish. My scope: [NUMBER] lessons averaging [LENGTH]. Give me the per-lesson time budget, the batching strategy, and the realistic production calendar at [HOURS PER WEEK].

My lessons pair narration with [VISUALS: SLIDES / SCREEN RECORDINGS / ANIMATIONS]. Give me the sync workflow: script written against the visual sequence with cue markers, whether to generate one file per slide or per lesson (trade-offs for editing and updates), the timing padding rule so visuals never lag the voice, and the re-sync checklist when a single slide changes.

The killer feature of AI narration is cheap updates. Design my update workflow: how to change one sentence in a published lesson (regenerate the sentence or paragraph, splice, verify tone match), the changelog that tracks which lessons contain dated content ([EXAMPLES: PRICES, UI SCREENSHOTS, VERSION NUMBERS]), the quarterly content review that flags stale audio, and the re-publish checklist per platform.

Build the QA protocol for course audio: the full listen against the script, learning-specific checks (are numbered steps distinct, do definitions land clearly, does emphasis fall on the concept not filler words), the error log format, and the learner-simulation pass: one full lesson at 1.5x speed, since many learners listen accelerated and clarity must survive it.

Package my audio for [PLATFORM: TEACHABLE / KAJABI / UDEMY / LMS]. Cover: the file specs and loudness target, whether the platform needs audio embedded in video (and the minimal-effort video format if so: slide plus waveform or static card), transcript and caption generation from my scripts, and per-platform quirks to check. Output the packaging checklist per lesson.

My completion data shows drop-off at: [LESSONS AND TIMESTAMPS]. Paste the scripts for those sections ([PASTE]) and diagnose the narration causes: density spike, monotone stretch, missing signposting, a confusing explanation, or misaligned expectations from the lesson title. Prescribe the script fix for each and note which need full regeneration versus a sentence splice.

Mine my learner questions and comments for narration problems: [PASTE QUESTIONS / SUPPORT THREADS]. Cluster them: concepts my narration explained poorly (rewrite candidates), terms learners misheard (pronunciation sheet updates), pacing complaints, and content gaps (new lesson candidates). Output the prioritized fix list with the script change for each.

My course reviews mention the narration: [PASTE REVIEW EXCERPTS]. Analyze the pattern: what learners praise (protect those qualities in future production) and what they criticize (fix list). Draft my response templates for narration-related reviews, and extract the strongest audio-quality quotes for my sales page social proof.

Learners from [MARKETS] are enrolling. Build the localization case: which course to translate first based on [DATA], the script translation approach that preserves teaching rhythm, ElevenLabs multilingual generation with a matched voice, the localized pronunciation sheet, and the revenue model: pricing and platform for the [LANGUAGE] version. Include the pilot plan: one module first, measured.

Run my annual course audio review: completion trends by module ([PASTE DATA]), accumulated fix list status, whether my voice and settings still fit the course positioning ([ANY REBRAND / AUDIENCE SHIFT]), competitor course audio quality in [NICHE], and the verdict per course: maintain, refresh narration, or full re-record with an upgraded approach. End with next quarter’s production priorities.