Explain key capabilities and limitations in plain language.
Check claims against original, current sources.
Use AI with academic integrity, privacy, and appropriate disclosure.
Course outline
01
How generative AI behaves
Understand prediction, confidence, and common failure modes.
02
Verification and judgment
Practice checking evidence, bias, and relevance.
03
Responsible school use
Apply privacy, integrity, and disclosure rules to realistic scenarios.
OPEN LESSON
Open lesson: How generative AI behaves
Before automating a task, define the result you need and the evidence that would make it trustworthy. In this course, the goal is: Evaluate an AI answer, verify its claims, and explain when AI should or should not be used. AI can accelerate a draft, comparison, or exploration, but it does not know your policies, full context, or the cost of being wrong. Begin with a low-risk case and keep the earlier version so you can compare, explain, and reverse the decision.
Define the deliverable and an observable quality criterion: Explain key capabilities and limitations in plain language.
Set context, allowed data, and human review before generating: Check claims against original, current sources.
Check the output and document the final decision: Use AI with academic integrity, privacy, and appropriate disclosure.
Responsible authorVidAcademia Editorial Team
ReviewVidAcademia Learning Quality
MethodOutcome-first design, primary-source review, guided practice, and a human-verified final decision.
SOURCES AND EVIDENCE
Learn with references you can verify.
We select official sources and explain what each supports.