Personalized Learning Using AI

An AI-based training platform optimizes knowledge acquisition, cuts training costs (boosts your ROI), and makes it a point of competitive differentiation for your business. Let’s see where to start to apply AI personalized learning in your business.

Example of AI-based Personalization in Corporate Learning

The importance of effective workforce training is becoming critical.

To cope with this issue, the forward-looking leaders already started to adopt progressive tools, including AI in talent management and continuous education.

The management of the US-based air medical transport company (Air Methods, a 2,800-person helicopter company in Englewood, Colorado), understood the importance of well-organized continuous education to train pilots.

When using traditional webinar-based training, every employee used to receive the same course, regardless of their learning speed and current skills. Such a method required a lot of time, drew little engagement, and didn’t motivate pilots to grow. So the company adopted an aviation training system built on Artificial Intelligence.

Process of Implementing Personalized Learning Using AI

Results after Implementing Personalized Learning Using AI

• According to the company leaders, the implementation of AI personalized learning in corporate training not only boosted the effectiveness of the training course but also helped to attract talent.
• The company could also significantly reduce their training and onboarding costs by cutting half the number of personal instructor-led training classes together with the duration of their onboarding program from 10 to 5 days. 

What is Personalized eLearning?

Personalized learning is an educational approach to address the individual learning needs and interests of each learner.

The goal of personalized online learning is faster knowledge acquisition and better knowledge retention.

According to the L&D Global Sentiment Survey, the two hottest topics in online education now are personalization and Artificial Intelligence.

But why?

By personalizing your learning content with the help of AI, you make the learning process faster and more effective. 

In its turn, this leads to shorter training time, reduced training costs, and higher productivity of the trained workforce. 

Features of Personalized Learning Software using AI

The major components of the personalized learning platforms that can be customized for each learner are:

• Relevant information. Using only relevant content that is based on learner’s interests leads to faster and better results.

• Preferred eLearning content types. By detecting the preferences of each learner, the system builds a personalized path majorly using preferred content types (video, audio, text, etc.) to increase engagement.

• Individual pace of learning. Thanks to adjusting the learning pace, every learner gets the necessary amount of time needed to master every topic or skill.

• Corresponding difficulty level. Using the right tools, especially an AI-powered skill map, the system more quickly identifies the competency level and learning gaps that impede progress, allowing learners to achieve the desired results faster.

• Immediate feedback. Thanks to more frequent and immediate feedback, learners can understand their performance and progress in real-time. It happens through formative assessments, quizzes, and knowledge checks.

Benefits of AI in Personalized Learning for Any Business

With more companies facing the challenge of upskilling and reskilling or arranging an effective professional education and licensing for their workforce, personalized learning has become a proven method that can bring success to businesses in a scalable manner.

And the most obvious benefits that using personalized learning software brings to business are:

• Speed up the professional education process and increase its quality.

Licensing and professional education require a lot of time, which negatively affects the working process. 

But personalized learning makes learning targeted. Learners receive tailor-made information they require to fill gaps and achieve learning goals quickly.

Also, the information is usually bite-sized, which makes classes short and increases knowledge retention.

Such an approach encourages employees to self-direct their training and favors creating a culture of continuous learning within a company.

• Boost engagement and motivation that leads to higher productivity.

When adult learners can control and self-direct the learning process, they are likely to feel more motivated. 

Learners pick up lessons skipping unwanted information that doesn’t correspond to their role, need, experience, or skill level. Besides, they take a proactive role in their education and study at a comfortable pace. 

That’s why personalized learning is a good choice to introduce professional learning on a regular basis.

• Reduce training hours and boost ROI.

Profit margin growth is the result of higher productivity and shorter training time. 

Personalization usually implies a bite-sized learning (microlearning) method. So learning at the working place becomes shorter and quicker. It equates to less learning time and fewer training payroll hours.

Besides, personalized learning information is consumed faster and with better results. So employees spend less time on training and demonstrate equal or even better outcomes.

How to Get Started with AI Personalized Learning in a Company?

Deep personalization that includes key aspects of training is performed with the help of algorithms. And even though traditional algorithms can perform this task, their possibilities are limited. 

Only AI-powered systems can provide a high level of personalization that improves over time. The reason is the AI engine constantly refines recommendations without the intervention of developers or admins by updating themselves based on previous outcomes.

Personalized learning with AI encompasses all the core aspects of online training:

• personalized learning path;
• relevant content based on knowledge level, skills, interests, and goals;
• automated knowledge checks;
• prediction of knowledge gaps;
• proactive learners’ support;
• tutoring, etc.

Here is an example of AI-powered training software with individual learning paths and personally recommended content.

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Setting up personalized learning with an AI engine requires the following steps:

Step 1. Create a skill matrix.

To determine the learner’s knowledge and then build a learning profile, start with creating a skill matrix.

For this, skills should be described by humans once. It can be done by analyzing employees’ CVs with the help of text analytics. Or employees can fill in a simple form with skills in rows and knowledge levels in columns.

And then these data will be parsed by a machine.

As a result, every learner will have an individual profile that reveals their current knowledge level and skills.

Step 2. Aggregate data about the learning background.

However, knowing only skills will not give a complete picture. Artificial Intelligence captures, gathers, and analyzes employees’ learning backgrounds from different sources within the company.

This can be easily performed with the help of xAPI. With xAPI you will collect information about employees’ previous and current learning experience from LMS, LXP, TMS, HRIS, etc with a great degree of detail.

Step 3. Use the AI engine to find patterns.

Having aggregated all the data, the AI spots trends and builds patterns for each learner. 

These data give admins or an L&D team valuable insights about learners’ performance and educational preferences.

In the next step, this information will be used to suggest the most relevant content.

Step 4. Build a training course based on AI-driven recommendations.

By identifying learning trends, the system can provide learners with study materials in the formats and of the difficulty level that best suit their learning preferences and skills. 

For example, AI informs an admin that a certain employee uses short videos as the major content resource and tends to watch about Technical SEO . Based on this data, the recommendation engine starts suggesting this learner more videos instead of a textual format, all around the preferred topic.

Thanks to getting highly relevant content in a preferred format, more learners succeed in the training course.

Step 5. Measure and assess regularly to adjust the course.

Learners start training using an LMS or LXP. The performance of each learner is measured and assessed regularly. 

The AI engine takes the latest results of a learner and adjusts the course. And this cycle constantly repeats.

Any business that implements AI in training courses gets a strategic advantage thanks to the possibility to detect missing skills in the workforce and arrange effective upskilling and reskilling to address this problem in time.

5 Pillars of Personalized Learning using AI-powered LMS/LXP

AI-based knowledge checks to detect the current knowledge level and gaps.

To discover a learner’s educational background and knowledge gaps, an AI-based system starts with a knowledge check.

This is a test that has no predefined consequence of questions. Every next question depends on a previous learner’s answer. In the case of correct answers, the difficulty level of questions constantly grows. In the case of an incorrect answer, the difficulty level lowers. 

The AI-powered LMS or LXP tests learners’ knowledge regularly to guide and adjust learning activities in time. 

After understanding the baseline proficiency, the learner is given a personalized learning path corresponding to the current knowledge level.

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AI-selected training content to address individual learning goals

Artificial Intelligence tracks how a learner is performing and progressing in the course. The previous performance is also taken into consideration. 

Then AI uses the gathered data to detect the level of learners’ proficiency and their interests.

The next step that AI makes is finding and recommending corresponding learning materials. So the course starts from the tasks revealing the learners’ knowledge level.

The proper selection of the content translates into faster knowledge acquisition and higher productivity of the workforce after completing such training. 

When it comes to the personalization of content types, modern LXPs and LMSs can also suggest multiple content types. 

For example, some learners get information better when watching short videos. Others learn quickly through reading. So the course will be majorly built on these preferences. 

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AI-created learning path to target detected knowledge gaps

After detecting your knowledge, interests, and goals as well as selecting relevant content, AI builds a non-linear learning path.

Non-linear means not predefined, chosen by learners by their needs and allowing them to complete the course at their own pace. 

Learners are not forced to learn what they already know. They skip certain segments of a course and start learning what they actually need. 

This is a key to higher motivation and engagement. Besides, it saves a lot of valuable time that will be spent on work thanks to efficient to-the-point learning. 

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AI-powered tutoring to provide proactive assistance in learning

AI virtual assistants and chatbots for education now act like tutors giving proactive recommendations about learning materials and assisting during the process.

They teach by simulating a human tutor like in the learning app Duolingo and can also remind learners about assignments and due dates.

Another aim of AI assistants is the selection and recommendation of the right courses for learners based on the topic, skills, duration, goals, and other factors. 

Besides suggesting optimal courses based on the user’s profile, it can also recommend educational information in the public domain (TED, HBR, BBC, etc) or help find a human tutor when needed.

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AI-enabled feedback to give a responsive assessment immediately

AI algorithms guide a learner through the learning path giving an immediate responsive assessment. 

For example, a learner struggles with a certain topic, there can be additional tools like pop-up hints, dictionaries, calculators, etc.

Meaningful and immediate feedback helps learners understand how they are progressing, detect weak and strong points, and get information that addresses their weaknesses.

An immediate machine-driven assessment also excludes a direct interaction with an admin or instructor, which represents an effective trial-and-error method of problem-solving ( eliminating the fear and hesitation of direct interaction with an instructor).