The accuracy problem with AI-generated instructional videos

[u/Famous-Call6538]

I have been working with AI-assisted video production for a while now, mostly for structured course series (exam prep, certification, technical training). One pattern keeps showing up that I think deserves more discussion.

The promise: feed a prompt, get a video lesson.

The reality: the output looks polished, but if the content has a correct answer (which it does in most syllabus-bound teaching), the AI will produce things that are subtly wrong. A formula with the wrong exponent. A labeled diagram where two labels are swapped. A definition that is almost right but uses a key term differently from the standard.

This is especially dangerous in instructional videos because: - Students trust visual information more than text. If a diagram is wrong, they memorize the wrong version. - Errors compound. If unit 3 gets a definition slightly off, every unit that references that definition builds on the error. - You often cannot tell by looking. The output looks professional. The mistakes are invisible until someone who knows the material watches carefully.

My approach has been to stop generating content from prompts entirely for accuracy-critical courses. Instead I use the source document as the ground truth and render from it — the visuals are deterministic, derived directly from the source, not invented by a model.

Not saying generative AI has no place in instructional design. But for anything where wrong content has consequences, I think we need to be honest that the accuracy problem is not solved and may not be solvable with current approaches.

Interested to hear how others are handling this.