Recently, our founder Manoj Kulkani sat down with Fiona Leteney of Fosway Group to discuss Realizeit and the future of Adaptive Learning.
Fiona has worked in the online learning market for over two decades. Before that, she worked in a physical classroom and experienced the challenges of teaching without an adaptive platform. She often had to deliver education down the middle, losing faster students who were bored, and slower students who weren’t understanding. Today, she is a senior analyst for Fosway.
Together they talked about a number of topics, including:
Fosway’s research on learning systems
Realizeit’s place in the learning market
What is Adaptive Learning?
Where does Adaptive Learning fit in the learning ecosystem?
What is the role of AI in Adaptive Learning?
Founded in 1996, Fosway is named after an ancient Roman road in Britain. Their analysts work extensively with corporate clients to discern the realities of the market and provide insights into the future of HR, Learning, and Talent.
Fosway is perhaps most famous for its 9-Grid system, a market analysis model used to understand the relative positions of solutions and providers for talent and learning. The grid classifies companies based on their performance and their potential and provides actionable insights on how to maximize the value of a provider in any particular section of the grid.
Each of the 9 squares of the grid provides value. The placement of a provider in the grid indicates not so much the value of a provider as it does the way in which users can maximize that value.
The 9-Grid system provides information about providers along five dimensions:
Performance
Potential
Presence
Total Cost of Ownership
Future Trajectory
As a learning provider, Realizeit falls under Fosway's research. They classify it as a Potential Challenger in the learning systems market. This puts Realizeit in the left-middle of the 9-grid system.
Continuous personalization
Support for Adaptive Learning Pathways
AI support for the automatic creation of microlearning
According to the 9-Grid system, a Potential Challenger like Realizeit is middle range in terms of potential and lower range in terms of performance.
Being middle-range in terms of potential means that Realizeit meets the needs of a majority of enterprise-class customers. It provides a broad solution that covers a range of common cases.
Being lower range in terms of performance does not mean that Realizeit is a poor performer. On the contrary, the fact that Realizeit is on the grid at all means that Realizeit delivers a quality product. The placement indicates that the organizations best suited for using Realizeit will have lower performance needs than other, more expensive systems.
Realizeit has come a long way since it was founded in 2007.
Manoj, the founder of the company, had a goal: he wanted to shape learning to individuals, rather than the other way around. He wanted to make learning better and engage people in the process of fully realizing their potential on a daily basis.
The company started out creating learning experiences for the elementary and middle school levels. It has since expanded to adult-level learning, including employee training, onboarding, upskilling, and more.
Realizeit seeks to maximize the time spent learning. If you only have 5 minutes to learn, how do you make the best use of those 5 minutes?
Realizeit also seeks to bring efficiency, outcomes, and agility:
Efficiency in terms of the value of time spent learning.
Measurable, concrete, bottom-line performance outcomes.
Agility in changing and reorienting company strategies.
For each of these aspects, Realizeit’s AI-driven platform provides support.
Adaptive learning is an approach to training and education that uses AI and machine learning to deliver targeted teaching content to learners precisely when they need it. By building a profile of each learner, including their strengths, weaknesses, and previous knowledge, adaptive learning can drastically reduce the amount of time wasted on redundant learning.
Compared with traditional learning methods, adaptive learning can be highly efficient. This is particularly valuable for organizations that spend large quantities of time and money in training each year.
Adaptive learning plays an important role throughout the entire learning ecosystem. Ideally, it should integrate with an organization’s LMS and LXP, allowing for greater use of their data in delivering learning content.
This means that adaptive learning plays a role across different training needs, as well as throughout the entire learning lifecycle.
Businesses can use Adaptive Learning to improve training for a wide variety of purposes, such as:
Employee onboarding
Upskilling
Compliance training
Some organizations can't put people on the job until they verify that they have job readiness. However, some employees come in with a certain amount of job readiness to begin with.
A one-size-fits-all training program thus wastes countless hours if it trains people who already have the requisite experience. It would be much better if a training program could adaptively verify what employees know and don't know so that they can simply fill in the gaps where needed.
Adaptive learning can save up to 50% of time spent learning by removing knowledge redundancies like these. And that is precisely what Realizeit aims to accomplish.
One key idea that Manoj pointed out during the podcast was that learning takes place across an entire learning life cycle, rather than at just a single point.
Learning starts with onboarding. Then it progresses to ongoing and reskilling learning. But at the same time, it also needs to be on-the-go learning.
The truth is, that learning encompasses all of these steps, acting as a whole.
Realizeit promotes engagement across all learning moments: adaptive onboarding, adaptive reinforcement, adaptive reskilling, and more.
AI is a technology that is now being used for a wide range of applications, more than just for generative AI tools like ChatGPT. It is particularly effective for making learning more efficient. Realizeit uses state-of-the-art AI to power its adaptive learning platform.
Recommending learning content
Constructing learner profiles
Assessing prior knowledge
Determining learner strengths and weaknesses
Assessing the effectiveness of different pieces of content
Additionally, Realizeit is now experimenting with integrating generative AI to help with learning content creation and curation as well.
As described above, adaptive learning technology applies across multiple training demands, and throughout the entire learning life cycle, to reduce redundancies and make learning more efficient. It personalizes learning to fit the learner, rather than expecting the learner to conform to the structure of the course.
Personalization is no longer a “nice to have” option for learning. Just as personalization has become an expected part of virtually every digital experience, it is now an expected part of learning.
Not all businesses have accepted this mindset yet. Manoj said he looks forward to the day when we can drop “modern” and “adaptive” from learning because those properties will simply be assumed.
Realizeit realizes this mindset by providing modern, adaptive AI learning technology. We help organizations deploy intelligent, agile, and performance-oriented learning experiences.
Interested in these topics? Learn more by watching Manoj’s and Fiona’s conversation here.