One of the dangers of constantly writing blog posts about the same topic is slowly inoculating oneself to the objections running against the viewpoint you present. So we wanted to devote this post to addressing one question: what separates AI from other L&D trends that have come and gone?
On the one hand, it’s a powerful technology that has the potential to revolutionize the way we learn. On the other, it’s a tool that can be easily converted into a gimmick. More significantly, there’s a real concern that the technology is too nascent and undeveloped to be of much use to learning professionals today.
So what, if anything, differentiates AI from other slightly disappointing trends in the learning and development space?
To answer this question, we'll take a closer look at two such recent trends: microlearning and gamification.
Microlearning emerged in the last decade as a way to deliver bite-sized pieces of content to learners. The idea was that learners would be more likely to complete a lesson if it was shorter and easier to digest.
Microlearning was seductive because it was backed by cognitive load theory, which suggested that our brains can only handle a certain amount of information at a time. The strategy itself was a best practice in the industry for over 15 years before LMS companies began adopting it as a platform format.
Good instructional designers were practicing this technique all along—but the buzz began when companies developing course creation tools started touting it as a key feature.
When microlearning emerged as a mainstay of authoring tools and learning platforms at the end of the last decade, it seemed to promise a revolution in learning: greater digestibility, higher completion rates, more ingrained understanding.
As powerful as the microlearning technique is, though, it didn’t quite live up to the hype. It can’t deliver nuanced ideas, as complex topics required more time and space to explain. More pressingly, its widespread adoption didn't significantly increase lesson completion rates—instead, shorter lessons lowered the bar for what constituted completion.
Similarly, gamification attracted the L&D world because of a potential for greater engagement. By structuring learning modules around point systems, rewards, or other game-like progressions, companies found they could get people to invest more in training. No surprise--we're hardwired to achieve, to problem-solve, to reach a goal.
Like microlearning, though, gamification turned out not to be the silver bullet for employee engagement many presumed it was. Gary Stringer of gethownow.com summarizes the problem well:
"We tell you one thing [gamification] isn’t, and that’s a substitute for good content. If nobody’s engaged with your 300-page PDF on becoming a better sales rep or 9 hours of compliance training videos, there’s not a leaderboard long enough to get them reading every word."
Sure, gamification promises tangible benefits--but only if we apply the principles correctly.
So what makes AI different?
First and foremost, AI is a technological innovation with enormous transformative implications. It makes a lot of cognitive work much more efficient. It can offload grunt work, such as formatting, tagging, and organizing content, which frees up human designers to focus on more important conceptual level work.
AI also has the potential to personalize learning to a degree that was previously impossible. It can analyze data about learners’ behavior and preferences, and use that information to create customized learning paths that are tailored to each individual.
Granted, there are concerns that AI might fall short in some ways. There is a risk that content creation will become derivative and uninspired if designers rely too heavily on AI-generated content. There is also a danger that designers will use AI as a crutch and lose the ability to innovate new and exciting learning experiences.
These concerns are valid—we shouldn’t dismiss them out of hand. But there’s a crucial distinction between the rise of trends like microlearning and gamification and the emergence of AI.
AI represents an actual change in technology, a new capability that didn’t exist before and that is going to radically alter how we develop lessons. And yes, the technology’s quite new. But we can demonstrate its helpfulness. If anything, AI paired with other approaches offer greater potential benefits than any of these strategies used in isolation--AI can adapt a microlesson in real time and understand the concision needed for the relevant audience, or fine-tune a gamified element to meet the learning style of a particular employee.
While gamification and microlearning have their strengths, they're methodologies. Baking them into our systems as the be-all-end-all of design, as some platforms did, was never workable.
To understand this difference, it’s helpful to look at the history of eLearning. In the early days, designers had to code everything by hand. This made it difficult and time-consuming to create eLearning courses, and it limited the types of interactions and media that could be used.
But then tools like Adobe Captivate came along, and suddenly designers could create eLearning courses using drag-and-drop interfaces. This was a significant shift in technology that made it much easier and quicker to create eLearning courses.
After that came platforms like Rise that made it even easier to create eLearning courses using a responsive design approach. This was another significant shift in technology that made it possible to create eLearning courses that worked well on any device.
These were both concrete improvements to technology—they delivered real value by changing fundamental aspects of how lesson creation functioned.
Microlearning and gamification, meanwhile, were theories transposed onto technology. Which isn’t to discount their value—but above all else, they're techniques paired with pre-existent tools, not new tools altogether.
AI has the potential to revolutionize how we develop and deliver content, from personalized learning experiences to immersive simulations. It's important to approach AI with caution and to use it as a tool rather than a replacement for human creativity and innovation. But by combining the efficiency of AI with the creativity of human designers, we can create impactful and engaging learning experiences.
If you want to validate (or invalidate) this claim about AI in learning for yourself, you can do so right here. Test out Mindsmith.ai and decide: is this adding value to the lesson creation process or not? After you give our tool a whirl, respond to this blog with your thoughts—we’d love to know what you think.
The Mindsmith Team