AI and eLearning: personalizing courses with technology

A few months after his exploit, the latest applications of artificial intelligence promise to transform almost all sectors of the economy. Training and eLearning are no exception. This technology is able to provide tools and methodologies that make learning more interactive, personalized and effective.

At the heart of this transformation are several AI applications that are redefining how educational content is created, distributed and consumed. Among the various benefits, there is that of personalization of learning: sophisticated AI systems analyze student data to adapt the content to their specific needs, level of competence and learning style. This not only improves student engagement but also ensures that the material is more relevant and useful for their learning journey.

AI-powered educational chatbots are becoming increasingly common, offering immediate responses and personalized support to students, facilitating more fluid and interactive learning.

Predictive analytics, another key application, allows educators to predict student performance and identify areas for improvement, allowing for timely and targeted interventions. These technologies, together with automatic content generation and recommendation systems, are opening new horizons in eLearning, making the educational experience richer, more engaging and personalized than ever.

Customization needs in eLearning courses

Personalization has become a key word in the world of eLearning. Courses that adapt not only to the knowledge level, but also to the learning style and pace of each student are highly appreciated for their greater effectiveness. In an era characterized by diversity and inclusiveness, AI plays a crucial role in meeting these needs. Thanks to AI, eLearning courses can now be designed to dynamically adapt to individual preferences, offering learning paths that are not only informative but also motivating.

This need is motivated by the different learning styles that each student has. Different students may have different preferences on how to receive information: with real examples, with general and theoretical rules, or with practical examples to practice on. Some people may prefer written texts or interactive videos; still others can do their best when the teaching is given by a physical person to whom you can ask live questions.

This customization therefore goes beyond the simple selection of content and extends to the mode of presentation, the type of activities proposed and even the feedback provided. For example, for a student who learns best through visual interaction, AI can suggest materials rich in graphics and videos. For another who prefers hands-on learning, he can propose interactive exercises or simulations. This level of personalization ensures that each student receives an optimized learning experience, maximizing the effectiveness of the course and maintaining high interest and participation.

AI technologies and their applications in eLearning

Let's try to better understand what the different AI technologies are. This technology has experienced a major acceleration in recent years. A first major distinction is that between analytical AI and generative AI. The first was developed to analyze a large amount of data and find connections between them using the most advanced statistical investigation techniques. The second is able to produce new contents, which have internal coherence and which can be interpreted as a language capable of conveying contents and information.

Below is a rough list of the functions that AI can perform:

• Machine Learning
• Natural Language Processing
• Artificial Vision
• Intelligent Robotics
• Recommendation Systems
• AI Chatbot
• Neural Networks and Deep Learning
• Predictive Analysis
• Robotic Process Automation
• Emotional Artificial Intelligence

With machine learning, computers can learn and improve from experiences without being explicitly programmed. This technology is widely used in various industries for predictive analytics, pattern recognition and decision making.

Natural language processing (NLP) allows machines to understand, interpret, and respond to human language in useful ways. It is critical for applications like virtual assistants, machine translation, and sentiment analysis.

Computer vision allows computers to interpret and understand the visual world. Used in facial recognition, medical image analysis, and surveillance systems.

Intelligent robotics allows robots and industrial machinery to learn, adapt and perform tasks autonomously. Used in industrial manufacturing, space exploration and home care.

Recommender systems are algorithms that suggest products, services or information to users based on their preferences and past behavior. Fundamental in e-commerce, streaming platforms and online services.

AI chatbots are programs that simulate human conversations for customer service, tech support, or entertainment. They are increasingly popular on websites, messaging apps and call centers.

Neural networks and deep learning are structures inspired by the functioning of the human brain. These are used for complex tasks such as speech recognition, medical diagnosis and consumer behavior analysis.

Predictive analytics uses data, statistical algorithms and machine learning techniques to identify the probability of future outcomes based on historical data. Applied in finance, marketing, meteorology and beyond.

Robotic Process Automation (RPA) allows you to automate repetitive and rule-based tasks in various business sectors, improving efficiency and reducing errors.

Finally, emotional artificial intelligence has the task of recognizing, interpreting and responding to human emotions. Used in marketing, healthcare and to improve human-machine interaction.

Creation of training content with AI

AI is revolutionizing the process of creating training content, making it more efficient and adaptable. One of its most promising applications is automatic content generation. Using artificial intelligence, it is possible to create tailored learning materials, such as quizzes, exercises, and even videos, that adapt to students' specific needs. This tool not only reduces the workload of educators but also ensures that each student receives personalized learning material based on their skill level and learning style.

Another fundamental aspect is the use of AI for the analysis of learning data. These systems can examine student interactions with learning material, identifying patterns and providing valuable insights into how to improve content. If a certain topic is difficult for most students, AI can suggest the creation of supplementary materials or more effective teaching alternatives. In this mode, AI does not replace the work of the Instruction Designer, the architect in charge of designing the training course. On the contrary, it supports the task and allows us to delve deeper into those aspects that could improve its effectiveness.

AI can also be used to continuously update and refresh content, ensuring that courses remain relevant and up-to-date with the latest industry information and trends. A course produced before a regulatory update or an important publication on the subject could be updated at reduced costs and in less time.

Personalization of the training experience with AI

Personalizing the training experience through AI is not limited to content creation alone; it also extends to the interaction and involvement of students in the learning process. AI technologies, such as recommender systems and voice recognition, offer a more intuitive and engaging learning experience.

AI recommendation systems, for example, can suggest courses or additional materials based on students' interests and previous interactions, similar to how streaming platforms suggest movies or TV series. This not only improves engagement but also helps students discover new topics of interest. Additionally, speech recognition and machine translation make courses more accessible to students of different languages ​​and abilities, eliminating language barriers and improving inclusivity.

Another key aspect is the use of AI for sentiment analysis, which can evaluate students' responses and feedback to better understand their emotions and reactions, thus allowing the teaching approach to be adapted in a more empathetic and sensitive way to their needs.

These technologies, together with virtual reality and simulations, create a rich and immersive learning environment that goes beyond the traditional eLearning experience.

Subjects suitable for the intervention of AI technologies

The use of artificial intelligence in eLearning is not limited to specific disciplines. The current success of generative AI also lies in its versatility. With proper training, an AI is capable of conversing on a wide range of topics. What are the best methods for training Artificial Intelligence is still the subject of research by innovative laboratories and startups.

However, it must be said that some subjects benefit particularly from integration with these technologies. Subjects like math and science, for example, can leverage AI to offer complex simulations and interactive problem-solving, allowing students to explore abstract concepts in more concrete and interactive ways.

In fields such as linguistics and language learning, AI can provide speech recognition and machine translation tools, facilitating pronunciation practice and language understanding. Humanities disciplines can also benefit from AI, for example through the analysis of historical or literary texts, offering new perspectives and insights.

AI also has a significant role in professional and technical disciplines, such as engineering and programming, where it can be used to create hands-on learning environments and realistic simulations. This versatility demonstrates how AI is not only a tool for improving teaching efficiency, but also a means for enriching and diversifying the learning experience across a wide range of disciplines.