Personalized Learning Using AI: Features and Benefits

According to the L&D Global Sentiment Survey, nowadays, the biggest buzzwords in the e-learning domain are Artificial Intelligence and Personalized Learning.

Personalized learning stands for the learning approach when each user gets the training path and materials that fit their goals, level, and interests. Thanks to this method, learners acquire new knowledge better and faster, which has already been approved by many case studies.

As a result, businesses also benefit from personalized learning, because their workforce needs less time for the training process and can promptly delve into working tasks. 

AI-based Personalization Case in Corporate Learning

Many leaders have started to understand that continuous education in Talent Management is crucial in our ever-evolving business environment.

Let us consider the real case of the US-based helicopter company with 2,800 employees. It previously used the legacy training program for its pilots with unified webinars. The management was not content with the results because of low motivation and engagement. The company decided to implement the new AI-based Aviation Training System to boost the effectiveness of their corporate learning.

First, the system tested every learner to define their level by various quizzes and games. Based on this assessment, AI built an individual training path for each employee to address their specific needs, for instance, in understanding strict safety regulations.

When a pilot struggled with some subjects, the new platform encouraged them to revise the topic using alternative scenarios.

​After finishing a course, a pilot had to complete the test. If the results reach the needed grade, a learner will be able to start the new module.

​Ultimately, the company could decrease the onboarding process from 10 to 5 days, and the number of lessons with an individual coach was also cut down by two times. ​Additionally, the up-to-date approach not only helped optimize the budget for corporate training and improve upskilling but also attracted more talents eager to work in a progressive company.

Features of AI Personalized Learning

• Relevant materials. Each employee usually lacks some specific gaps. There is no one unified tutorial that would be a perfect match for the whole staff. One Business Analyst needs to fill in the gap in Data Analysis, while another prefers to improve their Agile Methodology expertise.
• Preferable type of content. Our brain works and absorbs new information differently from person to person. Some people acquire knowledge better when listening to podcasts, others can remember some facts only by watching videos, and finally, a lot of people need to write down the thesis to be able to bear them in mind. Imagine, you often play short videos about cloud technologies. An AI algorithm detects that and offers you such content in the future. 
• Personalized speed. Again, people are different, and some of them prefer digging into tons of learning materials for hours, while others endorse micro-learning as a working approach for them.
• Propel level. This tool works great after a company has prepared the skill matrix for each role. For that, an employee needs to describe their competencies only once using a special form and then, the machine will process this information. Even in the same positions, employees might have different levels of competencies and various skill gaps. That is why they need a personalized learning path according to their professional grade. 
• Prompt feedback. The system offers versatile quizzes to check the new knowledge, which helps learners better understand their progress and turn back to the materials they didn’t manage to acquire.

Why This Method Works

When adult people are free to handle their upskilling process by themselves in a personalized form, it likely boosts their motivation and encourages the culture of continuous learning in organizations. 

Switching the focus from teaching to learning develops leadership skills and stimulates employees to take more responsibility both in training and real working tasks.

The system suggests convenient velocity, small portions of information, and the ability to skip unnecessary materials that don’t correspond to your competencies and professional ambitions. 

Moreover, an AI-based system continuously improves its recommendations without the participation of software developers or administrators. Its algorithms collect the previous data, analyze results, and design new content sets for users. 

87% of companies reported they expected many skill gaps shortly. Other research predicts that up to 375 million people will have to go through the reskilling process and acquire a new profession. 

Forward-thinking companies are constantly looking for the tools to predict new roles and required skill sets. They would like to be prepared for the changing landscape in advance to get a competitive advantage. 

Systems with AI-powered predictive learning analytics can warn an L&D (Learning and Development) department about potential workforce gaps that your organization might face for the next few years. Of course, you need to connect your software with any database with the average tendencies in a desired domain to leverage this opportunity.