Input: lesson plan or outline and presenter reference
Output: a lesson video with captions and a recap question
Aspect: 16:9 for classroom; 9:16 for microlearning
Build log
1. Cut to one objective
I write the objective as one observable action. If the title has “and”, I split the lesson.
2. Build a Canvas storyboard
Each node gets one knowledge point, one visual, and one line. I avoid generated diagrams when an approved slide is already correct.
3. Lock the presenter
I reuse the same face, clothing, and lighting reference across lessons. This makes course modules feel like one teacher, not random avatars.
4. Estimate before generating
Four 5-second shots per lesson: Agnes Video is 40 credits per lesson and 400 credits for 10 lessons. Seedance 480p is 90 credits per shot, or 3,600 credits for 10 lessons.
5. Add captions and a check
I add captions in the external editor and end with one question. If a viewer cannot answer it, the lesson did not teach one objective.
Complete execution record
Task boundary and source gate
Treat AI course video generator as a deliverable, not a definition. The job receives lesson plan or outline and presenter reference and owes a lesson video with captions and a recap question in 16:9 for classroom; 9:16 for microlearning. Write the rejection reason first, then turn it into the first hard constraint.
Require the objective verb, terminology sheet, worked example, assessment item, and the source of any claim. If a slide contains two ideas, split the node before spending credits. Do not let a caption introduce a claim that the approved source does not contain. A pretty diagram cannot override wrong terminology.
The imgmov route
Break the outline into Canvas nodes before generating media: one observable objective, one example, one check. Slides, notation, and approved screenshots are references. Subtitles, review questions, and progress markers go to the external editor after picture lock.
WAI describes captions as synchronized text of speech and non-speech audio needed to understand content, and states that automatic captions are not sufficient. Review captions against muted playback before delivery.
Lock the variables that cannot drift
Lock terminology, notation, example numbers, instructor identity, clothing, background light, diagram style, and caption format. A course should feel like one teacher with one method.
Put the locked fields at the top of the prompt, not at the end: One objective, one example, one check. If every 5-second shot uses Agnes Video at 2 credits/second, 10 lessons with four shots cost 400 credits; the same clips cost 3,600 credits on Seedance 480p. Use Canvas for lesson structure and your external editor for captions and music. Save that prompt with the reference, ratio, and model so the next run starts from a decision instead of a guess.
Step-by-step build path
1) Cut to one objective: I write the objective as one observable action. If the title has “and”, I split the lesson. Leave one checkable artifact from this step; do not start the next until it exists. 2) Build a Canvas storyboard: Each node gets one knowledge point, one visual, and one line. I avoid generated diagrams when an approved slide is already correct. Leave one checkable artifact from this step; do not start the next until it exists. 3) Lock the presenter: I reuse the same face, clothing, and lighting reference across lessons. This makes course modules feel like one teacher, not random avatars. Leave one checkable artifact from this step; do not start the next until it exists. 4) Estimate before generating: Four 5-second shots per lesson: Agnes Video is 40 credits per lesson and 400 credits for 10 lessons. Seedance 480p is 90 credits per shot, or 3,600 credits for 10 lessons. Leave one checkable artifact from this step; do not start the next until it exists. 5) Add captions and a check: I add captions in the external editor and end with one question. If a viewer cannot answer it, the lesson did not teach one objective. Leave one checkable artifact from this step; do not start the next until it exists.
Change one named variable per rerun: prompt, reference, camera, duration, model, ratio, or export crop. If two variables change together, a better output cannot be reused because nobody knows which fix worked.
Review gates and evidence
Mute the video and read the captions. Then play it once and ask whether the learner can complete the ending check without rewinding.
Keep source, approved wording, rejected version, correction, credit cost, and final crop next to the asset. The record exists so the next reviewer can reproduce the decision without a chat thread.
Failure diagnosis and retry ladder
If learners fail the check, cut a concept instead of adding a recap. If notation drifts, return to the approved slide. If captions lag, split the sentence at a phrase boundary.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
Versioning and handoff
Deliver classroom, mobile, and captioned versions separately. Save the objective, reference set, prompt, and check as the next module's starting point.
The folder is boring on purpose: source, approved reference, generation settings, caption file, platform cuts, QA screenshots, and one correction line. A reusable asset is boring in the right way.
Why this is not a generic answer
The imgmov advantage is the chain: Asset Library preserves the subject reference, Canvas fixes shot order and first/last frames, the workspace routes Agnes to Seedance, Kling, or Veo only after proof, and your external editor adds captions and CTA without regenerating the media.
Do not let a caption introduce a claim that the approved source does not contain. A pretty diagram cannot override wrong terminology. If the request only asks what ai course video generator means, a search page is faster. This page is useful when someone must deliver a lesson video with captions and a recap question under real constraints.
Frequently asked questions
How long should a lesson be?
One objective and one example. Short modules beat a long dump.
The workflow is specific about its stopping point: it starts with lesson plan or outline and presenter reference and stops at a lesson video with captions and a recap question. In imgmov, upload the source, lock it as a reference, set 16:9 for classroom; 9:16 for microlearning, run one proof, and save the passing settings. If a reviewer cannot tell which version is approved, the workflow has failed even when the media looks good.
How much do 10 lessons cost?
With four 5-second Agnes Video shots each, 400 credits. The same shots on Seedance 480p are 3,600 credits.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
Where should captions go?
In the external editor after the picture lock. This lets you fix wording without regenerating media.
The check is not “does it look AI-nice?” It is: Mute the video and read the captions. Then play it once and ask whether the learner can complete the ending check without rewinding. Then the source, approved copy, rejected version, correction, and final crop stay in the same handoff folder.
How do I keep one instructor?
Reuse one reference set: face, clothing, hair, background, and light.
If learners fail the check, cut a concept instead of adding a recap. If notation drifts, return to the approved slide. If captions lag, split the sentence at a phrase boundary. Change one named variable, keep the old version, and record the credit cost. If the same failure repeats, fix the reference or scope rather than asking the prompt for forgiveness.
Can I use my slides?
Yes. Reduce text and upload them as references instead of asking the model to invent a new slide.
The imgmov advantage is the chain: Asset Library preserves the subject reference, Canvas fixes shot order and first/last frames, the workspace routes Agnes to Seedance, Kling, or Veo only after proof, and your external editor adds captions and CTA without regenerating the media. The handoff is reusable only when the source, reference ID, prompt, model, cost, rejection reason, and approved cut are linked. That chain is what makes the next ai course video generator task faster.