7 Challenges AI-powered Adaptive Learning Helps Address Today

It is predicted that AI in the educational domain will rise at over 10% CAGR by 2032 because of the growing interest in personalized learning. This trend provides new opportunities for innovative business ideas and large investments. In this article, we want to highlight some challenges organizations successfully tackle by leveraging AI adaptive learning systems.

High Learning & Development Budget

People acquire new knowledge faster if they have mentors. According to National Mentoring Day, almost every learner endorsed this approach. But imagine that every employee has a personal tutor. The staff number will increase two times, which means double expenses for companies. 

An AI-powered adaptive learning software can tackle this challenge by building a personalized curriculum for each learner based on their needs, interests, and progress. It is a kind of digital mentor that has already perfectly known you.

Scaling Limitations

When we talk about the traditional learning model, applying a personalized approach is possible only in small groups. A mentor can’t allocate enough time for tailored communication with each learner in big corporations. Companies can struggle when they significantly increase their workforce and plan to scale their educational program. Lack of individual training can lead to many issues, as mentioned before. 

An AI-based adaptive learning platform helps overcome this inhibitor by covering employees’ needs and interests without human supervisors at each stage. A manager can participate by request. Thus, the number of learners can be unlimited. It enables companies to scale their training programs without significant extra investments. 

Outdated Competencies

Continuous upskilling and reskilling are vital to keep pace with an ever-evolving business environment. Various hard skills become outdated rapidly, and organizations need to control and manage this process to stay ahead in their industries. 

AI-powered adaptive software enables companies to keep track of job market tendencies, updating and adapting training materials for changing conditions. For instance, the system can upgrade a legal training course, adding a new case law. It ensures professionals will always stay abreast of up-to-date practices, which gives employers a competitive advantage.

A government agency in the United States designed a 20-year forecast, taking into account self-driving vehicle development, innovative solutions for the public transport system, and demographic changes. Findings include job positions that might be in high demand in the future, for example, AI analysts, autonomy engineers, and urban/rural mobility managers. According to these insights, the organization can restructure and prepare its workforce in advance for new market conditions. 

Low Learning Engagement

The traditional learning approach stands for a standard curriculum for all personnel. The bottom line is that employees can have different levels of various competencies even in the same positions. 

An AI-powered adaptive learning platform eliminates this problem by creating a bespoke training path for each individual. For example, software engineers might get various coding tasks based on their level of proficiency in each programming language. 

People don’t suffer from monotonous exercises, because the process is adjusted for each learner’s interests. Moreover, the platform corrects this program according to the progress, not to unified timeframes and requirements. For instance, the US Air Force and the US Army have already implemented a training system that tailors lessons to each learner according to their strengths.

This method helps build trust between an organization and employees because they appreciate an individual approach. As a result, the company gets a more loyal workforce and a continuous development culture. 

Inefficient Use of Time

Studying irrelevant materials can frustrate people who get used to dedicating time effectively. It causes poor time management if it goes about a learning program. Employees are trying to avoid a boring training process and lose valuable knowledge as well. 

Study shows that by implementing adaptive learning, companies can cut time for Learning & Development (L&D) by 40-60%. People spare themselves from wasting time on information they have already absorbed. Simultaneously, an individual approach boosts their engagement, as pointed out above. 

Low Level of Acquired Knowledge

You need to apply or revise your knowledge to save them. Otherwise, this information will move into a passive state. As a result, the knowledge gap can occur repeatedly. It is a usual property of human memory, and you can effectively manage it.

Adaptive learning software monitors different knowledge gaps using particular algorithms. If some breaches are detected, it suggests techniques to refresh the information. Ultimately, learners get long-lasting skills they can apply urgently in a force majeure situation.

Lack of Effective Assessment Process

The assessment process can be inapplicable because it gives grades for grades. Both managers and learners don’t see tangible results in such studying. 

One more of the great features of adaptive learning platforms is that they provide managers with performance metrics of each employee, show their strengths and weaknesses, and recommendations about which skills to improve. 

In Conclusion

The modern business landscape demands continuous adaptation and improvements. Skill sets needed five years ago can be outdated today, as are various educational certificates as well. Both public and private sectors need new approaches to encourage in-house continuous learning culture. That is why the adaptive learning method seems so attractive. It emphasizes regular little changes, boosts employees’ engagement and progress, and helps organizations scale rapidly, optimizing their L&D budget.