From course recommendations to virtual chat coaches – artificial intelligence is changing the way we inform and educate ourselves, both professionally and personally. Instead of group workshops and generalized training company wide, more significance should be placed on individualized development and adaptive learning. Here you can find out what this is, how it works best and how learning platforms use AI to personalize upskilling.
Finding the right training, meeting the diverse learning needs of employees, and then reaching everyone in the office, in the field, and on the shop floor are the biggest challenges faced by HR developers and L&D managers these days. And the importance of these tasks is growing.
Due to the shortage of skilled workers and fluctuations in the job market, continuous training is evermore important to retain employees and secure their future professional growth. Nevertheless:
- 68% of managers and HR staff are currently concerned that their workforce is poorly or moderately prepared for future challenges.
- In line with this, 70% of employees state that they do not have the skills required for their work.
One reason for this is a lack of learning transfer.
Although knowledge is taught in seminars, workshops and courses, it is rarely put into practice. However, the transfer design of training courses plays a decisive role in ensuring that employees apply what they have learned in their daily work.
So what can you do to optimize learning transfer?
One way to find a solution: Adaptive Learning.
Basics: What Is Adaptive Learning?
Adaptive learning describes the customization of training to the learner's level of knowledge, learning preferences and needs. A personalized learning process increases the efficiency and effectiveness of learning.
With this learning concept, employees are offered precisely the learning content and methods that are most effective for their personal development and work.
Personalization can be applied in:
- Learning content: Support in selecting the learning offer based on what knowledge is really needed for the person and role.
- Learning paths: Sorting of content and creation of learning routes that are aligned with the respective needs and requirements.
- Learning methods: Personalization of training opportunities and suitable learning formats that contribute to individual development.
In order to control individualization, companies usually rely on special software. This uses complex algorithms and enables customized upskilling for all employees through the use of artificial intelligence (AI).
AI-based processes are now helping to implement adaptive learning in an even more targeted manner in order to impart knowledge efficiently and consolidate it in the long term through individual training support.
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7 Components: How Does Adaptive Learning Work?
At its core, the adaptive learning process is about the improved effectiveness and efficiency of further training measures. But what exactly needs to be considered for adaptive learning to really succeed?
Adaptive learning works particularly well when these seven key aspects are taken into account:
1. Assessment of the Initial Level
An adaptive learning system presupposes that the individual knowledge levels and abilities of all learners are known. In most cases, these are determined by questionnaires, quizzes or self-assessments.
2. Personalization of the Learning Path
Based on the initial assessment, personalized learning paths are developed for all learners. These are not only individualized, but also flexible enough to be continuously adapted to the respective learning progress.
3. Flexibility of the Learning Content
The individual learning elements can be variably combined in adaptive learning. By diversifying courses and formats, the learning experience assures that everyone can develop the skills relevant to them with the right content at their own pace.
4. Support in Real Time
Adaptive learning works particularly well when learners can receive immediate feedback on their performance or the necessary assistance. Misunderstandings can be clarified immediately and learning becomes more effective.
5. Analysis of Progress
Data is the core of adaptive learning. Information on learning progress is collected continuously in order to derive new measures. Those responsible must have access to it at all times and learners should also be able to see their successes.
6. Motivation Through Interaction
Tailoring learning to the level and interests of the learner increases learning engagement. Gamification elements, such as learning points or interactive quizzes, and small "learning bites" support this effect.
7. Use of Technology
Adaptive learning systems are based on algorithms, artificial intelligence or machine learning. Learning platforms integrate these technologies and continuously develop them to improve the learner's experience.
Pros & Cons: Benefits and Downsides of Adaptive Learning
The pedagogical approach of adaptive learning is comparable to personal coaching. A person (or technology) specifies which content should be trained in which exercises and is always available for tips and assistance.
Like a personal coach, adaptive learning has both advantages and disadvantages.
Advantages of Adaptive Learning
It can be said that adaptive learning improves the quality of further training. In detail, individualized training pays off through:
- Efficient Training Management
Employees use their available time for relevant further training and do not "waste" it on training that is neither important for them personally nor for their day-to-day work. - Increased Learning Effectiveness
Personalized training is tailored to the individual's skill level, learning needs, and relevance to job or personal development. This combination ensures the best possible knowledge transfer and retention. - Greater Motivation to Learn
Thanks to the high degree of personalization, employees experience greater learning success. This results in greater (intrinsic) motivation to learn and greater commitment to training. - More Independent Learning
Greater motivation to learn promotes self-directed learning. Individualized, value-added training encourages employees to develop their skills on a voluntary basis. - Consolidated Knowledge
As learning units can be accessed at any time and repeatedly incorporated into learning paths, all learners have the opportunity to engage with the content until it is understood. - Unlimited Accessibility
Automated course suggestions and AI-driven learning with virtual coaches have the great advantage by being availalbe 24/7. Software solutions are accessible anytime and anywhere and also reduce the workload.
Disadvantages of Adaptive Learning
Just like with a personal coach, it may take time to find the right one. This also applies to adaptive learning.
- Less Human Interaction
Due to the use of technology and the focus on individuals, adaptive training sometimes lacks an interpersonal component that even the best AIs cannot (yet) replace – and probably will not. Tools try to solve this by offering functions that support blended learning or social learning. - Limitations Due to Tools
Not all HR professionals want to place their full trust in algorithms and AI. However, some platforms and systems are limited when it comes to individual configurations. Some solutions also allow you to control and create your own learning paths, for example. - Different Software Solutions
Adaptive learning is very flexible and is used in many different ways in professional development. As a result, there is no one-size-fits-all software solution. The choice can be difficult. It is important to ensure that the specific needs of the company and its employees can be met.
In the Company: How Does Adaptive Learning Work With a Learning Platform?
Adaptive learning is valuable for companies precisely because it is tailored to the personal and professional needs of employees. In addition, it develops skills in a more targeted way than standardized training.
In order to support employees' skills development as efficiently and effectively as possible, companies are increasingly relying on adapting individualized, further employee development through learning platforms.
Digital software solutions offer the greatest flexibility in training design and are integrating more and more possibilities for individualization – especially thanks to the rapid developments in the field of artificial intelligence.
For the implementation of adaptive learning, a platform can ideally:
- Individually suggest training and courses,
(based on personal prior knowledge and existing skills) - Offer personalized support and
(for example through real-time feedback and AI-based chat-bots) - Send learning reminders.
(either automated or triggered by administrators)
In addition, modern platforms often offer different learning methods and course formats and are technically barrier-free, (available to all employees anytime, anywhere).
The more diverse and variable the e-learning offering is, the better adaptive learning can be integrated into the digital learning strategy.
Adaptive Learning With Masterplan: Individual Growth Map & Personalized AI Coaches
Masterplan's Learning Engagement Platform supports adaptive learning through the targeted use of algorithms and AI. From "know-what" to "know-how", each individual employee receives the right support to successfully drive their own development.
Power Skills: Learning What Is Important
In a guided self-assessment, learners assess their own skills in a fun way. Based on this information, they receive a unique recommendation for which Power Skills (the 7 universal skills that everyone will need in the future) they should develop further.
Individual development progress is mapped with learning points and skill levels in a personalized growth map. This also contains personalized course recommendations for each user.
Masterplan PACE: Real Skills Instead of Knowledge
With the personalized AI coaching provided by Masterplan PACE, learners can apply what they have learned directly in practice. After selected lessons, a chat starts where learners actively apply their new knowledge. PACE adapts a selective role, answers questions and provides immediate feedback.
All employees receive individual support that ensures successful learning transfer.
Conclusion: Adaptive Learning & AI as the Way Forward
Adaptive learning fulfills many acute requirements for professional and individual training – both from the perspective of companies and employees. This makes it one of, or perhaps even the most promising method for future-proof upskilling.
The continous development of artificial intelligence will ensure that the quality and degree of individualization of further training is also improved.
Nevertheless, adaptive learning will not automatically and immediately close every skill gap.
On the contrary: L&D managers and HR managers will be even more in demand in the future to integrate new technologies into the existing learning landscape in a meaningful way and to manage upskilling measures in a targeted manner. If this is successful, professional development can make a major contribution to the success of an organization.
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Stefan Schulze
