Learning creation speed: longform learning, microlearning, and AI-enabled L&D
Learning teams have been placed at the forefront of solving thorny organizational problems. For many teams, there’s no single path forward. What there is, is a growing understanding that agility, more rapid testing, and efficient learning delivery to advance organizational thinking farther and faster.
One key aspect of optimizing training development is benchmarking the time it takes to develop corporate training courses. Benchmarking provides a valuable tool for measuring and analyzing the efficiency of training development practices, allowing organizations to optimize resources and improve training outcomes.
A historical issue with benchmarking learning creation is that there are many different formats that corporate learning can take. Everything from immersive VR modules, through off-sites, through educational prompts sent via SMS. After performing our own internal research and surveying all publicly available, we’ll focus on corporate learning forms that can be most readily tied to learning and behavior outcomes: traditional “longform” training (virtual or in-person), and more “in-the-moment” education, specifically microlearning.
In this guide:
Longform curriculum like in-person training, high-fidelity video, and even VR can be a time and cost-intensive process that is slow to create, slow to take, and slow to test. According to a recent study by the Association for Talent Development (ATD), the average 20-minute classroom instruction takes between 43 and 141 hours to create, depending on the complexity of the content and the level of interactivity required.
We should add, that this is only the first step in the process. Once the course is created, learners often have to travel to a specific location to take the training, which can be time-consuming and expensive.
Finally, traditional corporate learning can be slow to test to see what learning content works and what doesn't. This issue can be attributed to the linear and inflexible nature of traditional training courses, which often involve long development times and require significant resources.
The lack of customization to individual learning needs can result in a one-size-fits-all approach that fails to engage or motivate learners. This can lead to lower levels of knowledge retention and reduced return on investment for training initiatives. As a result of these challenges, organizations are increasingly turning to more agile and cost-effective methods of training development, such as microlearning.
Consumer expectations have shifted over the last decade. Learners expect media be delivered at the right place, right time, and with the right content. In short, personalization and relevancy of content are needed or learner attention can’t be grabbed and kept.
Luckily, many learning tech stack options that support personalization and relevancy also shrink the form factor of learning content. This exponentially speeds up the process of creating learning materials and testing them out.
In the case of Arist, we’ve found that learning teams are able save roughly 80% in resources needed for course development by utilizing messaging apps learners and learning teams already use to deliver bite-sized learning moments. As opposed to longform content that is almost always “pull” in delivery, “push” training provides several benchmarking-related benefits.
Because push education is designed to be delivered in bite-sized chunks, it can be created and tested much more quickly than traditional courses. This allows learning teams to be more agile, testing new content and making changes based on learner feedback.
Another benefit of push education is greater engagement and retention. By providing learners with personalized and relevant content that is delivered at the right time, push education is more likely to keep learners engaged and motivated. This, in turn, can lead to better knowledge retention and improved learning outcomes.
In addition to the benefits of "push" education and microlearning described above, generative AI can further increase the speed of course creation. Arist's new feature, Sidekick, allows learning teams to create drafts of courses in minutes by leveraging the power of AI.
While no generative AI output should replace learning team judgement and SME expertise, shifting many of the more tedious and repetitive portions of course building to AI enable learning teams to spend more time on greater value additions. In particular, we see learning teams able to focus more on the following strategic decisions when AI speeds up course creation:
Needs assessment (which can occur more regularly with faster course launches)
Discerning the proper triggers, delivery method, and timing for courses to be relevant
Discerning the proper metrics to link L&D to organizational results
Analyzing data about what works and doesn’t about existing L&D initiatives
Improving live courses
Creating more continuous learning “arcs” and filling gaps in learning coverage