AI Video: Sora & RunwayML is a structured, practical course covering Sora, RunwayML, HeyGen. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
AI toolsTool stack
OpenAI
Technology marks for AI Video: Sora & RunwayML, sourced from the CC0-licensed Simple Icons project.
Use Sora appropriately in a realistic, bounded task.
Use RunwayML appropriately in a realistic, bounded task.
Use HeyGen appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
Sora: Foundations
Apply Sora through capabilities, limitations, responsible use.
Lesson
Key terms
Revision question
Capabilities with Sora
capabilities, Sora, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a capabilities result produced with Sora?
Limitations with RunwayML
limitations, RunwayML, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a limitations result produced with RunwayML?
Responsible use with HeyGen
responsible use, HeyGen, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a responsible use result produced with HeyGen?
Instructional figureProcess flow
Capabilities: from context to evidence
Capabilities connects bounded input to a reviewed output in AI Video: Sora & RunwayML.
1Bounded input
Define the purpose, intended user and Sora constraints.
frames
2Capabilities
Compare representative inputs and record where performance changes.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Capabilities is credible only when the result can be traced back to its purpose, inputs and constraints. Capabilities is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 2
RunwayML: Working methods
Apply RunwayML through clear instructions, context design, verification.
Lesson
Key terms
Revision question
Clear instructions with Sora
clear instructions, Sora, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a clear instructions result produced with Sora?
Context design with RunwayML
context design, RunwayML, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a context design result produced with RunwayML?
Verification with HeyGen
verification, HeyGen, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a verification result produced with HeyGen?
Instructional figureContinuous cycle
Clear Instructions: from context to evidence
Clear Instructions connects bounded input to a reviewed output in AI Video: Sora & RunwayML.
1Bounded input
Define the purpose, intended user and Sora constraints.
frames
2Clear Instructions
Rewrite a vague request as a bounded specification, then compare outputs.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Clear Instructions is credible only when the result can be traced back to its purpose, inputs and constraints. Clear Instructions is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
HeyGen: Applied workflows
Apply HeyGen through research, creation, automation.
Lesson
Key terms
Revision question
Research with Sora
research, Sora, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a research result produced with Sora?
Creation with RunwayML
creation, RunwayML, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a creation result produced with RunwayML?
Automation with HeyGen
automation, HeyGen, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a automation result produced with HeyGen?
Instructional figureProcess flow
Research: from context to evidence
Research connects bounded input to a reviewed output in AI Video: Sora & RunwayML.
1Bounded input
Define the purpose, intended user and Sora constraints.
frames
2Research
Build a question matrix and inspect primary sources before synthesising.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Research is credible only when the result can be traced back to its purpose, inputs and constraints. Research is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
Sora: Quality and review
Apply Sora through evaluation, privacy, repeatable practice.
Lesson
Key terms
Revision question
Evaluation with Sora
evaluation, Sora, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a evaluation result produced with Sora?
Privacy with RunwayML
privacy, RunwayML, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a privacy result produced with RunwayML?
Repeatable practice with HeyGen
repeatable practice, HeyGen, ai-tools
In AI Video: Sora & RunwayML, which evidence best supports a repeatable practice result produced with HeyGen?
Instructional figureSide-by-side comparison
Evaluation: from context to evidence
Evaluation connects bounded input to a reviewed output in AI Video: Sora & RunwayML.
1Bounded input
Define the purpose, intended user and Sora constraints.
frames
2Evaluation
Build a test set with pass conditions, edge cases and failure categories.
produces evidence for
3Reviewed output
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Evaluation is credible only when the result can be traced back to its purpose, inputs and constraints. Evaluation is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable AI Video: Sora & RunwayML project using Sora, RunwayML, HeyGen.
Expected output: A working AI Video: Sora & RunwayML artefact plus an evidence-based self-review.
Tools: A suitable Sora environment, A plain-text decision log, Test data or realistic sample material
Production steps
Define the intended user, outcome and constraints.
Create the smallest complete result using Sora.
Test one normal case, one boundary case and one failure response.
Revise the work from the evidence and preserve before-and-after results.
Prepare a concise handover containing method, limitations and next step.
Success criteria
The output matches the stated outcome.
Inputs and decisions are reproducible.
Boundary and failure evidence is included.
Limitations and responsibility considerations are explicit.
Self-review
Can another learner repeat the method?
Did I test a difficult case?
Did I avoid unsupported claims?
Is the next action proportionate to the remaining risk?
Next step: Choose one weakness found during review and improve it before extending the Sora scope.
Glossary
Sora
A core concept or tool used in AI Video: Sora & RunwayML; its exact meaning is established in the relevant lesson.
RunwayML
A core concept or tool used in AI Video: Sora & RunwayML; its exact meaning is established in the relevant lesson.
HeyGen
A core concept or tool used in AI Video: Sora & RunwayML; its exact meaning is established in the relevant lesson.
This guide is generated from DigiLearn course material. Product versions, regulations and professional standards can change; consult the linked authoritative source before applying version-sensitive guidance.