u/Minute-Lobster553

I've been thinking about something lately that feels a bit counterintuitive. We've spent years evaluating AI voices by asking one question: "Does it sound human?" But I'm starting to wonder if that's even the right metric, especially for educational content. Human narrators add personality, emotion and variation, and we automatically assume that's better. But when I think about the technical courses I consume regularly, I'm not entirely convinced that's actually what I need.

If I'm learning about APIs, spreadsheets, cybersecurity or some other technical topic, I don't necessarily need a charismatic narrator. What I need is consistency. I need technical terms pronounced the same way every time, a predictable pace and explanations that don't suddenly speed up, slow down or emphasize random words. The weird thing is that AI voices are often surprisingly good at exactly that, not because they're more natural, but because they're less variable.

And that made me wonder if we've been optimizing for the wrong thing all along. Most conversations around AI narration are about realism. People compare voices and debate whether listeners can tell the difference between AI and humans. But maybe that's not the question we should be asking in the first place. Maybe we should be measuring comprehension instead.

After someone finishes a lesson, did they actually understand the material better? Did they retain more information? Did consistency end up being more useful than personality? The more I think about it, the more I suspect educational content might be one of those rare categories where being perfectly human isn't necessarily an advantage. Has anyone seen actual studies or run experiments around comprehension rather than naturalness?

I've been thinking about something recently that feels a bit backwards. Five years ago, translating an online course into 10 languages would have been a huge project. You'd need translators, voice actors, someone to edit everything, update subtitles, synchronize videos, QA the whole thing and then, after spending all that time and money, you'd still have to ask yourself whether there was enough demand in those markets to justify the effort. Localization was something you did later, once you had already proven that your course worked. But now I'm not sure that logic still makes sense anymore.

The cost of reaching new learners keeps going up almost everywhere. Paid acquisition is more expensive, organic distribution is harder, social platforms are saturated, and everyone is competing for the same attention. At the exact same time, the cost of localization is moving in the opposite direction. Translation quality has improved dramatically, AI voices are getting better every few months, and entire workflows that used to take teams of people can now be done in a fraction of the time. We're getting surprisingly close to a world where localizing a course into multiple languages is no longer a major investment. In some cases, it's starting to look almost insignificant compared to the cost of finding new customers in the first place.

And that's what made me realize that maybe we've been treating localization as a growth strategy for too long. What if it's actually becoming infrastructure instead? We don't ask whether a website should work on mobile anymore. We don't ask whether software should support different screen sizes. Those things are simply expected. Maybe educational content is heading in the same direction, and publishing a course in only one language will eventually feel just as outdated. What's interesting is that this could completely reshape competition. Historically, language was a natural barrier. If you published in English, you mostly competed for English-speaking audiences, and expanding globally required a lot of resources. But if localization becomes cheap enough, that barrier disappears. Suddenly your competition isn't just creators in your own market anymore, it's creators everywhere. At the same time, your potential audience also becomes everyone.

The funny thing is that I don't see many people talking about this yet. Most conversations around AI are still focused on generating more content, but I wonder if the bigger shift is actually distribution. Maybe the most important thing AI is changing isn't how we create educational content, but where that content can realistically exist. I'm curious how other people think about this. At what point does localization stop being a growth initiative and simply become a standard part of publishing educational content?

We recently discussed course production, narration, and voiceovers with a colleague and started questioning how much learners actually experience the content the way creators imagine.

Most course creators, myself included, tend to imagine learners sitting down, pressing play, and paying close attention to every lesson. Yet when I look at my own habits and the habits of people around me, that's rarely what happens. Most online courses are consumed at 1.5x, 1.75x, or even 2x speed. People listen while commuting, walking the dog, doing household chores, exercising, or switching between multiple browser tabs. They're often focused on extracting information as efficiently as possible rather than experiencing the content exactly as it was produced.

That realization made me question something I've always taken for granted: are we dramatically overestimating the importance of narration quality? To be clear, I'm not talking about obviously bad audio. Poor microphone quality, distracting background noise, awkward pauses, inconsistent volume levels, and mispronounced terms can absolutely hurt the learning experience. But beyond a certain threshold of quality, I wonder how much learners actually notice the difference. Do they care whether the narrator sounds charismatic, warm, and expressive? Do they care whether the voice belongs to a human or whether it was generated by software? Or are those distinctions far more important to creators than they are to learners?

The reason I keep thinking about this is that course creators spend an enormous amount of time perfecting narration. We listen to the same lesson dozens of times. I mean we notice every awkward sentence, every unnatural pause, every slight change in tone. Eventually we become hyper-aware of details that learners may only encounter once. And not only once, but often at nearly double speed. Meanwhile, when I think about the courses that have had the biggest impact on me personally, I struggle to remember much about the narrator at all. What I remember are the ideas, the explanations, the examples, and the moments when something finally clicked. I can recall lessons that changed the way I think, but I often couldn't tell you whether the voice behind them was particularly engaging or not.

So now I'm wondering whether we've been optimizing for the wrong thing. Maybe narration quality is similar to website performance: below a certain standard it creates friction and people notice immediately, but once it becomes good enough, further improvements deliver rapidly diminishing returns. Perhaps learners care far more about clarity, structure, pacing, and relevance than they do about the finer details of how the content is voiced. I'm genuinely curious what others think. If you create or consume online courses regularly, do you believe narration quality is still a major differentiator? Or have we reached a point where many creators are investing significant effort into something that most learners barely notice?

I don’t think instructional design is disappearing. But I do think the version of instructional design many people built careers around is changing very quickly. For years, being a strong ID often meant being strong at production. If you could build polished modules, create interactions, clean up SME content, and ship courses efficiently, you were valuable. That was the role.

But AI is rapidly lowering the barrier to producing learning content. It can already help generate objectives, quizzes, scripts, voiceovers, lesson structures, summaries, and even full draft learning flows in minutes. The ability to simply produce a course is becoming less rare and less valuable on its own. At the same time, organizations are becoming more focused on business outcomes rather than learning outputs. Most leaders do not actually care whether a course exists. They care whether onboarding becomes faster, mistakes decrease, adoption improves, teams ramp quicker, managers perform better, or employees change behavior in measurable ways.

I think the IDs who will continue growing are the ones who can connect learning to performance, diagnose operational problems, influence stakeholders, simplify workflows, and identify when training is not actually the right solution. The work starts looking less like course production and more like performance consulting. Tool skills still matter. Knowing how to build effective learning experiences still matters. But production alone no longer feels like enough to differentiate someone in the field. And honestly, I think that may be healthy for L&D long term.

Curious how other people in L&D are feeling about this shift. What do you think will matter more for IDs over the next few years: production skills or performance/business thinking?