Avatar Video Workflows for Course Creators

Avatar Video Workflows for Course Creators

TL;DR

Course creators and instructional designers using AI avatar video need to decide which course moments deserve a synthetic presenter, not whether avatars should replace teaching. The constraint is trust at production volume: every clip still needs verified source material, rights review, accessibility review, and one clear learner action. Start by benchmarking a short intro, recap, prompt, or update; keep filming when credibility, nuance, or demonstration carries the lesson.

The Real Constraint Is Not Filming. It Is Trust at Volume

The useful question is not simply "Can I make course videos without filming?" It is "Can I create more course-adjacent clips without weakening learner trust?" AI avatar video may reduce filming for some updates, but the real constraint is review capacity: every generated clip still needs source fidelity, accessibility checks, rights clearance, and a clear learner action before it belongs in a course.

That trust framing is a production-risk judgment, not a claim that avatars improve learning outcomes. Narrow, reviewable media choices are easier to manage than sweeping replacements for instruction. Columbia University's Center for Teaching and Learning frames effective educational video around clear instructional intentions and research-based design principles, which is a better starting point than production novelty [[8]](#citation-8). Use instructional role as an editorial filter before generating a clip: decide whether the asset is meant to orient, recap, prompt, explain, or update the learner. Accessibility should also be considered during media design, because W3C WAI guidance for audio and video media covers captions, transcripts, descriptions, and accessible media-player support as part of making media usable [[7]](#citation-7).

Avatars are strongest for repeatable, reviewable learning moments: the short pieces around a lesson that orient learners toward the next action. Filmed or live instruction remains stronger for credibility-heavy lessons, nuanced demonstrations, coaching, critique, and moments where instructor judgment matters more than production speed.

Use avatar video when the asset is:

Short enough to review carefully.

Scripted from verified course material.

Useful even if the avatar is not the main teacher.

Easy to update when the course changes.

Low-risk if a learner watches it without surrounding context.

That makes avatar video a micro-video workflow: intros, summaries, nudges, answer explanations, office-hour reminders, localized previews, and course-update clips. It is a weaker fit for long-form lecture capture, nuanced demonstrations, live coaching, or lessons where instructor credibility depends on spontaneous explanation.

Which Course Assets Are Worth Turning Into Avatar Clips?

Before choosing a tool, sort course assets by learner value. Novelty is a poor filter. Reuse, clarity, reviewability, and accessibility are better ones. Captions, transcripts, and media alternatives should be part of the asset decision instead of cleanup after publishing [[7]](#citation-7).

Select assets by their instructional role: what they help the learner notice, remember, or do next. A good avatar candidate is aligned to a course objective, has a narrow learner action, and can be checked against approved course material; that mirrors course-video guidance that starts from instructional intent before production technique [[8]](#citation-8). The table below is a practical production heuristic for deciding what to test first, not proof that an avatar improves learning outcomes.

Course asset Avatar fit Publish, test, or avoid

--- ---: ---

Lesson intro High Publish when it sets context and names the next learner action.

Module recap High Publish when it repeats key distinctions without adding new claims.

Practice prompt High Publish when the instruction is short enough to verify against the assignment.

Course update High Publish when the date, policy, screen, or curriculum change is current.

Sales teaser Medium Test only if course claims are verified and attribution, disclosure, and commercial-use checks are complete for the tool, marketplace, and jurisdiction where the clip will appear.

Localization snippet Medium Test only when a fluent reviewer checks meaning, tone, pronunciation, captions, transcript wording, and cultural fit.

Full lecture Low Avoid when the clip is too long to review in one pass or when the instructor's presence carries the lesson.

Sensitive coaching Low Avoid if learner trust depends on live instructor judgment, critique, empathy, or personal feedback.

Complex screen demo Low to medium Use screen recording plus narration unless a presenter layer clarifies the task.

Giggy is an unlimited AI generation platform for images, videos, and speech where creators can generate as much as they want without paying for credits [[1]](#citation-1). In this workflow, it is most relevant for short image-plus-audio avatar snippets, voice reads, and image concepts when iteration volume matters more than long-form course-video packaging.

A sensible first project is a short lesson intro or recap. A risky first project is converting an entire flagship course into synthetic presenter videos before you know how learners respond.

The Avatar-Video Workflow: From Course Objective to Publishable Clip

The workflow should move in one direction: learning objective first, synthetic media last.

1. Choose the learner job

Write one sentence that states what the clip does for the learner.

Examples:

"Prepare learners for the difference between gross margin and net margin before Lesson 3."

"Remind students to submit the practice worksheet before the live critique."

"Explain the new software-interface change without re-recording the full module."

If the clip does not reduce confusion, reinforce memory, or improve course navigation, skip it.

2. Pull from verified course material

Use the existing lesson transcript, slides, worksheet, or FAQ as the source. Do not let the avatar script invent the teaching point.

For each clip, keep a short source note:

Field Example

--- ---

Course source Module 2, Lesson 4 transcript

Claim owner Instructor or subject-matter reviewer

Last checked 2026-07-01

Risk level Low, medium, high

Publish location LMS lesson page, email, sales page, community post

This matters because course creators remain responsible for instructional accuracy even when the delivery is synthetic.

3. Write for spoken clarity

Avatar scripts should be shorter and simpler than lesson notes. Aim for one idea, one learner action, and one clean ending.

A reliable structure:

```text Context: "In this lesson, you are going to..." Learner value: "This matters because..." Action: "Before you continue, do..." ```

For a recap:

```text "You just learned [concept]. The key distinction is [plain-language contrast]. In the next exercise, use that distinction to [specific learner task]." ```

Avoid dense definitions, stacked caveats, and long compound sentences. If the script needs footnotes, it is probably too much for an avatar clip.

4. Generate or record the audio

Audio carries much of the trust. Review the voice before generating the avatar.

Check:

Pronunciation of names, acronyms, tools, and technical terms.

Pace for new learners, not for you.

Tone: instructor, coach, peer, narrator, or character.

Pauses around the core concept.

Whether a localized version needs a human reviewer.

If you use a synthetic voice, verify consent and rights before using a real person's voice, likeness, or recognizable identity. Check the current terms, acceptable-use policy, and marketplace rules for the tool before relying on any voice-rights, likeness, synthetic-media, or safety-sensitive workflow detail; treat those checks as part of production rather than legal cleanup after the fact [[9]](#citation-9) [[10]](#citation-10) [[11]](#citation-11) [[12]](#citation-12).

5. Generate the avatar clip

Now choose the presenter format.

Common options:

Instructor avatar: works when the creator wants continuity for low-risk updates.

Brand character: useful for onboarding, reminders, or course mascots.

Neutral presenter: safer when you do not want to imply the real instructor personally recorded the clip.

No avatar: often better for screen demos, deep lessons, or sensitive feedback.

HeyGen and Synthesia are AI video platforms that publish product and pricing pages for avatar-video workflows [[3]](#citation-3) [[4]](#citation-4) [[5]](#citation-5) [[6]](#citation-6). Treat any vendor-stated capability as a starting point, then verify the exact duration, export, localization, collaboration, LMS, SCORM, embed, and plan-limit requirements your course workflow needs on the current official pages before budgeting.

6. Review the clip like a learner

Do not review only for whether the avatar "looks good." Review whether the clip helps the course do its job.

Use this loop:

Watch without captions. Is the point clear?

Watch with captions. Are terms spelled correctly?

Listen without video. Does the audio still teach?

Show it to one learner or team reviewer. What did they think they were supposed to do next?

Fix the script first, then regenerate.

If a clip fails, diagnose the script before regenerating. A weak teaching point rarely becomes clearer just because the presenter looks more polished.

7. Publish with context

Embed avatar clips where they support the lesson, not where they distract from it.

Good placements:

Above a lesson as a short orientation.

After a module as a recap.

Inside an assignment page as a prompt.

In a course community as a reminder.

On a sales page as a teaser, with accurate course claims.

In a localized landing page as a preview, after review.

For LMS use, verify file format, captions, transcript availability, accessibility requirements, and whether you need SCORM, analytics, or embedded updates [[7]](#citation-7). Do not assume plan-dependent requirements such as duration, export behavior, credits or minutes, collaboration, LMS/SCORM support, embeds, rollover, or enterprise controls from memory; verify the exact current wording on the official product, pricing, and help pages before committing [[4]](#citation-4) [[6]](#citation-6).

How to Choose the Right Avatar Video Stack

Do not choose an AI avatar tool from the demo reel alone. Choose by the job your course workflow actually needs to handle.

Need What to evaluate Better fit

--- --- ---

Short intros and recaps Avatar duration, fast script-to-audio-to-avatar iteration, rights checks Short-form avatar workflow

Long presenter lessons Duration, editing, captions, review tools, export behavior Longer-form avatar platform or filming

Localization Script adaptation, course-term pronunciation, captions/transcripts, reviewer fluency, and support for the needed language workflow Platform with localization workflow

LMS publishing SCORM, embed behavior, captions, analytics, file handoff Training-oriented platform

Frequent course updates Regeneration cost, credit limits, version control, reviewer workload Low-friction iteration stack

Brand trust Avatar realism, disclosure, rights, reviewer approval Human filming or carefully governed avatar

Use this scenario table to turn the general criteria into a production choice.

Creator scenario Better starting stack Main tradeoff

--- --- ---

Solo creator refreshing module intros Short-form voice/image/avatar iteration You need fast variants more than enterprise publishing controls.

Cohort educator sending weekly prompts Short avatar clips or narrated slides Prompt clarity and update speed matter more than avatar realism.

Enterprise training team needing formal LMS handoff Longer-form avatar or training-oriented platform Verify governance, LMS export, review roles, embeds, duration, and plan limits on current official pages before budgeting [[4]](#citation-4) [[6]](#citation-6).

Creator localizing course teasers Localization-capable workflow plus fluent review Language support is not enough; review meaning, pronunciation, captions, and cultural fit.

Instructor filming flagship lessons Traditional filming or screen recording Credibility, demonstration detail, and live judgment matter more than synthetic presenter speed.

Choose short-form avatar snippets when the clip can be reviewed in one pass and does not need formal LMS packaging; the risk is publishing too many small clips without source control. Choose a longer-form avatar platform only after its current official pages confirm the exact duration, export, collaboration, LMS, SCORM, embed, and plan-limit requirements your workflow needs [[4]](#citation-4) [[6]](#citation-6). Choose narrated slides or screen recording when the visual task matters more than the presenter's face; the risk is making the asset feel less personal. Choose traditional filming when credibility, demonstration detail, or live instructor judgment is central; the risk is slower updates.

The decision path is conditional. Use Giggy for 10-second talking-avatar snippets from image plus audio when you need fast iteration on course intros, prompts, recaps, or teasers [[1]](#citation-1) [[2]](#citation-2). Evaluate longer-form avatar platforms when your course requires capabilities that are documented on the current official pages, such as longer presenter-video length, exports, LMS handoff, collaboration, or embeds [[4]](#citation-4) [[6]](#citation-6). Use filming or screen recording when the lesson depends on credibility, live judgment, complex demonstration, or learner relationship.

Before budgeting, verify the current vendor pricing page because plans, credits, minutes, export limits, language support, licensing, and attribution rules can change after publication [[2]](#citation-2) [[4]](#citation-4) [[6]](#citation-6). HeyGen's public pricing materials describe plan limits and credit mechanics, while Synthesia's pricing page describes plan and add-on details; treat those numbers as current-page inputs, not permanent assumptions [[4]](#citation-4) [[6]](#citation-6).

Unit economics check

This worksheet helps you compare avatar tools without inventing a universal cost-per-minute number. Use official pricing pages for current plan inputs, then run the calculation with your own production assumptions [[2]](#citation-2) [[4]](#citation-4) [[6]](#citation-6).

The budget question changes with the billing model:

Workflow Budget implication

--- ---

Credit- or minute-based avatar tools Discarded reads, failed variants, longer clips, exports, and localization versions may affect the amount of credit or minute capacity you need, depending on the current plan or API rules [[4]](#citation-4) [[6]](#citation-6).

Unlimited-generation workflows If the official product positioning supports unlimited generation for the relevant media type, the constraint shifts toward reviewer time, rights checks, quality control, and whether the output format fits the course asset [[1]](#citation-1).

Do not estimate cost from "number of course videos." Estimate from usable clips. This is a missing-input model, not a benchmark or price quote; verify current pricing, included capacity, rollover, attribution, and export rules on official pages before calculating [[2]](#citation-2) [[4]](#citation-4) [[6]](#citation-6).

Use your own inputs:

```text usable clips needed per month x average clip length x script/audio/avatar versions per approved clip x localization variants x reviewer rework rate = generation volume to budget for ```

Then collect these tool-specific inputs from official pages before committing:

Plan price [[2]](#citation-2) [[4]](#citation-4) [[6]](#citation-6).

Included credits, minutes, or generations [[2]](#citation-2) [[4]](#citation-4) [[6]](#citation-6).

Whether failed or discarded generations consume credits [[4]](#citation-4) [[6]](#citation-6).

Maximum avatar clip duration [[1]](#citation-1) [[4]](#citation-4) [[6]](#citation-6).

Export quality and watermark rules [[4]](#citation-4) [[6]](#citation-6).

Commercial-use and attribution terms from the current policy or terms page for the tool and publishing destination [[9]](#citation-9) [[10]](#citation-10) [[11]](#citation-11) [[12]](#citation-12).

Rollover or expiration rules [[4]](#citation-4) [[6]](#citation-6).

Team review, commenting, and workspace controls, if your workflow requires them [[4]](#citation-4) [[6]](#citation-6).

LMS, SCORM, caption, and transcript support, if your publishing environment requires them [[4]](#citation-4) [[6]](#citation-6).

Use this fill-in formula for cost per approved clip:

```text monthly plan cost / approved clips published that month = plan cost per approved clip ```

Use this fill-in formula when variants or localization drive generation volume:

```text approved clips x script reads per clip x avatar variants per script x localization versions x expected discard multiplier = generated assets to budget for ```

For illustration only, if your own tracked workflow used 3 script reads, 2 avatar variants, and 2 localized versions for one approved clip, you would budget for 12 generated assets before one publishable clip, before any rework multiplier.

The useful question is not "Which tool is cheapest?" It is "Can I afford to iterate until the clip is clear enough to publish?" The decision metric is cost per approved, reviewed, publishable clip, not cost per raw generation.

Where Giggy Fits in a Course Creator Workflow

In this course workflow, Giggy is most relevant when iteration is the bottleneck: testing voice styles, trying thumbnail or presenter concepts, producing 10-second talking-avatar snippets from image plus audio, and refining course-adjacent assets before committing to a final direction [[1]](#citation-1).

Giggy is especially relevant for:

Short lesson intros.

Module recap snippets.

Practice-prompt videos.

Course teaser hooks.

Localized micro-clips.

Presenter or character concept tests.

Voice style exploration before final recording.

Image concepts for course thumbnails, modules, or social promos.

Giggy's homepage positions the product around unlimited generation for text-to-speech, AI image generation, AI voice generation, and avatar video creation [[1]](#citation-1). Before budgeting, check the current pricing page for plan-specific billing, limits, or eligibility details [[2]](#citation-2).

In this context, text-to-speech means turning written scripts into spoken audio, AI image generation means creating image concepts from prompts, AI voice generation means producing voice outputs for narration or variants, and avatar video creation means using an image and audio to create a 10-second talking-avatar clip [[1]](#citation-1). Check Giggy's current pricing and policy pages before relying on plan, licensing, voice, likeness, or rights details [[2]](#citation-2) [[9]](#citation-9) [[10]](#citation-10).

Before using outputs in a paid course, verify the current license, attribution, download, and voice or likeness rules on Giggy's current customer-facing policy and pricing pages [[2]](#citation-2) [[9]](#citation-9) [[10]](#citation-10).

The natural Giggy workflow looks like this:

Generate or refine a course image concept for the presenter, character, or lesson visual.

Generate several speech reads of a verified short script.

Pick the clearest voice and pacing.

Use the image plus audio to create a 10-second talking-avatar clip, then treat it as a snippet rather than a full lesson video.

Compare variants for clarity, trust, pronunciation, and learner action.

Publish only the approved version.

The boundary matters as much as the fit: Giggy is a strong fit for 10-second talking-avatar snippets and high-volume creative iteration. If you need long-form lessons or formal LMS governance, evaluate tools whose official pages document those requirements, or use filming or screen recording instead.

Use Giggy first when the clip can fit a 10-second learner prompt, intro, recap, or teaser and the main job is rapid voice, image, or avatar iteration [[1]](#citation-1). Switch to longer-form avatar tools or filming when the asset needs extended explanation, formal LMS handoff, documented collaboration/export controls, or instructor credibility that depends on human presence [[4]](#citation-4) [[6]](#citation-6).

Rights, Accuracy, and Quality Checks Before You Publish

This checklist helps you avoid the expensive mistake: publishing a polished synthetic clip that teaches the wrong thing, uses an identity without proper permission, or confuses learners.

Product capabilities, pricing, exports, and policy limits should be verified on the official pages for the tool you plan to use [[2]](#citation-2) [[4]](#citation-4) [[6]](#citation-6). Evidence has a boundary: vendor pages can support what a product says it does, but learner trust, course quality, localization quality, and whether a clip belongs in your lesson still require hands-on review. W3C WAI's media accessibility guidance identifies captions, transcripts, descriptions, and accessible media players as relevant components for audio and video accessibility, so include those checks where your publishing environment supports them [[7]](#citation-7).

Instructional accuracy

This is an internal course review checklist, not vendor-verified evidence. The tool can generate the clip, but your team still has to verify the teaching point.

Is the script pulled from approved course material?

Did the instructor or subject-matter reviewer approve it?

Are dates, policies, product screens, and claims still current?

Does the clip tell the learner exactly what to do next?

Likeness, voice, and consent

Are you using a real instructor, student, customer, or contractor's likeness?

Do you have permission for the intended use?

Are voice clones or lookalike characters allowed by the platform and by your own agreements?

Would a learner reasonably think the real person recorded the clip?

Before relying on billing, subscription, creator-responsibility, voice, likeness, synthetic-media, or safety-sensitive details, check the current terms and acceptable-use policy for the specific tool directly [[9]](#citation-9) [[10]](#citation-10) [[11]](#citation-11) [[12]](#citation-12).

Commercial use, attribution, and disclosure

Can the output be used in a paid course?

Does the plan require attribution or watermarking?

Are there disclosure rules for synthetic media in your platform, jurisdiction, or industry?

Does your sales page clearly distinguish course outcomes from promotional examples?

Do not rely on memory for these details. Verify platform terms, course marketplace rules, and applicable jurisdiction or industry requirements before publishing [[9]](#citation-9) [[10]](#citation-10) [[11]](#citation-11) [[12]](#citation-12).

Accessibility and learner usability

Accessibility review protects learners who cannot rely on the same visual or audio channel you used while producing the clip.

Add captions or a transcript where your platform supports them [[7]](#citation-7).

Check captions for technical terms and names.

Avoid fast pacing in localized or beginner-facing clips.

Make essential video information available through text, captions, descriptions, or transcripts where your platform supports those formats [[7]](#citation-7).

Test on mobile if learners consume your course there.

Localization review

Use a native or fluent reviewer for meaning, not just grammar.

Check pronunciation of course terms.

Confirm examples make sense culturally.

Review captions and transcripts separately from the spoken audio.

Keep localized clips short enough to re-review after updates.

Avatar quality

Watch for lip-sync drift.

Watch the eyes and mouth on mobile.

Check whether gestures distract from the lesson.

Remove clips that feel deceptive, uncanny, or less trustworthy than a simple narrated slide.

Evidence Limits: What Public Sources Can and Cannot Prove

This evidence note helps you separate facts you can verify before buying from quality judgments you can only answer by testing your own course assets.

Use public sources for purchase and policy facts. Use your own benchmark for course-fit judgments.

Public product and pricing pages can verify vendor-stated capabilities, plan-dependent limits, credit or minute models, export behavior, and LMS-oriented feature claims [[2]](#citation-2) [[4]](#citation-4) [[6]](#citation-6). W3C WAI accessibility guidance can verify why captions, transcripts, descriptions, and accessible media-player support belong in the review process [[7]](#citation-7). Instructional-design sources answer a different question: whether the media choice starts from a clear teaching purpose rather than the novelty of a tool [[8]](#citation-8). Treat instructional purpose, learner activity, and design intent as editorial review criteria unless a course team has its own instructional-design standard.

Those sources can answer:

What a vendor says the tool can do.

How the current plan is described.

Which current policy pages still need direct review before publication.

Which accessibility or instructional-design checks belong in the workflow.

They cannot answer:

Whether a generated avatar clip will improve your specific course.

Whether the output preserves instructor trust.

Whether it pronounces your terms correctly.

Whether it fits your learners' context.

Whether it works cleanly in your LMS.

Whether it passes your team's approval bar.

Use public sources to shortlist tools. Use a benchmark to decide whether the tool can carry your course voice, review process, and publishing workflow.

Benchmark Before You Scale

This short benchmark keeps the tool decision from depending only on public feature pages or polished demos. Run the same course task through each candidate stack, then compare the outputs against your own learner, reviewer, and publishing constraints: source fidelity, pronunciation, captions or transcripts, visual trust, rights status, and the learner's next action.

Use one verified script and test:

One lesson intro.

One module recap.

One practice prompt.

One localized teaser, if localization matters.

One update note with a date, policy, or product-screen reference.

For each task, save the source script, generated audio, avatar output, captions or transcript, reviewer notes, and publish decision. That record turns the benchmark into a repeatable production test instead of a one-off demo reaction.

Inspect:

Script fidelity to the source material.

Pronunciation of course-specific terms.

Caption and transcript accuracy.

Avatar lip sync and facial movement on mobile.

Time from script to approved export.

Number of discarded generations.

Whether the clip tells the learner what to do next.

Whether the output meets your team's pass/fail bar for trust.

Define pass/fail before comparing tools:

Criterion Pass condition Fail condition Reviewer owner

--- --- --- ---

Script fidelity Clip matches approved source material. Clip changes, adds, or omits a key claim. Instructor or subject-matter reviewer

Pronunciation Names, acronyms, tools, and course terms are understandable. A key term is mispronounced or distracting. Instructor or fluent reviewer

Captions/transcripts Key terms and names are preserved in supported caption or transcript formats [[7]](#citation-7). Captions or transcripts confuse the concept, name, or instruction. Accessibility or publishing reviewer

Avatar trust Presenter supports the message without implying false human presence. Avatar distracts, feels deceptive, or weakens confidence. Course owner

Rights/likeness Likeness, voice, attribution, and policy checks are complete for the tool and publishing destination. Any permission, attribution, or disclosure question remains open. Operations or legal reviewer

Learner action Reviewer can name the next learner action without rereading the source. Clip sounds polished but leaves the learner unsure what to do. Instructional designer

Capture the decision record in the same format for every tool. Keep the first table focused on ownership and publishing status.

Asset tested Tool used Reviewer Publish decision

--- --- --- ---

Lesson intro

Module recap

Practice prompt

Localized teaser

Use the second table to record why the asset passed or failed.

Asset tested Pass/fail reason Rights/accessibility blocker Discarded generations

--- --- --- ---:

Lesson intro

Module recap

Practice prompt

Localized teaser

Treat vendor pages as evidence for stated capabilities, pricing, and plan limits. Treat your benchmark as evidence for whether the tool can carry your course voice, review process, and publishing workflow.

A First Clip You Can Make This Week

Start with one lesson intro. Do not automate the whole course.

Use this brief:

```text Course: Module: Lesson: Learner problem: One concept to preview: One action after watching: Source material: Reviewer: Publish location: Disclosure or attribution notes: ```

The first clip should pass three gates before publishing: verified source material, one clear learner action, and completed rights review plus the accessibility checks your publishing environment supports [[7]](#citation-7).

Example script:

```text "In this lesson, you will learn how to choose the right pricing model for your first digital product. The key is not picking the highest price. It is matching the price to the buyer's risk, the outcome you promise, and the support you can realistically provide. Before you continue, write down the one result your customer should get within the first week." ```

Generate one voice version and one avatar version, then add or verify captions/transcript support in the publishing tool before release [[7]](#citation-7). If you use Giggy for this first test, treat the avatar output as a 10-second talking-avatar snippet rather than a full lesson video [[1]](#citation-1) [[2]](#citation-2). Review it against the checklist. If it improves the learner's next step, make two more clips: a module recap and a practice prompt. If it feels unnecessary, keep the lesson as text or record it yourself.

The Bottom Line

AI avatar video is useful for course creators when it turns verified course material into short, reviewable learning moments. It becomes risky when it is treated as a blanket substitute for teaching, review, accessibility, or learner trust.

Use avatar clips where repetition, updates, localization, or short presenter presence creates real value. Use human filming where credibility, nuance, demonstration, or relationship matters more than production speed. Use Giggy when the work calls for many short-form voice, image, and 10-second avatar iterations before choosing the version worth publishing.

Citations

<a id="citation-1"></a>[1] Giggy homepage (https://giggy.ai/) <a id="citation-2"></a>[2] Giggy pricing (https://giggy.ai/pricing) <a id="citation-3"></a>[3] heygen.com (https://www.heygen.com/) <a id="citation-4"></a>[4] heygen.com - pricing (https://www.heygen.com/pricing) <a id="citation-5"></a>[5] synthesia.io (https://www.synthesia.io/) <a id="citation-6"></a>[6] synthesia.io - pricing (https://www.synthesia.io/pricing) <a id="citation-7"></a>[7] w3.org - av (https://www.w3.org/WAI/media/av/) <a id="citation-8"></a>[8] ctl.columbia.edu - effective videos (https://ctl.columbia.edu/resources-and-technology/teaching-with-technology/diy-video/effective-videos/) <a id="citation-9"></a>[9] Giggy terms (https://giggy.ai/terms) <a id="citation-10"></a>[10] Giggy acceptable use (https://giggy.ai/acceptable-use) <a id="citation-11"></a>[11] heygen.com - terms (https://www.heygen.com/terms) <a id="citation-12"></a>[12] synthesia.io - customer terms of service (https://www.synthesia.io/legal/customer-terms-of-service)