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Introduction to Artificial Intelligence

Artificial intelligence will be a game-changer in eLearning, and eLearning.health is an AI software development company that is ready to help its clients use AI to advance their education and training needs. With artificial intelligence systems, eLearning and continuing medical education (CME) courses can adapt in real-time to the trainee, ensuring that extra resources are devoted to challenging areas while also feeding curiosity on well-understood subjects. A personalized approach to eLearning can be based upon a wide variety of demographic information, behavior in the eLearning course itself, and specific responses to tests and exercises that are used by the course to evaluate knowledge and skills retention.

No matter how sophisticated the eLearning platform, traditional eLearning and online CME course content is presented identically to each user, and knowledge retention is verified through a standard test or series of exercises. This approach makes the learning process static. This standardization offers genuine value by ensuring that all learners cover all the necessary areas of study to gain mastery of the material as defined by the eLearning course, but it has drawbacks.

How Human Instruction Informs Artificial Intelligence in eLearning

In classroom instruction and tutoring, the learning process is fluid, and adapts to the strengths and weaknesses of each individual learner. Cues such as confused faces, extra time spent on tasks, and questions clustering around specific topics indicate to instructors topics that are particularly challenging to individual students. Conversely, students that quickly demonstrate understanding of the material by synthesizing it into their own language, asking questions that indicate a desire for greater depth of topic understanding, and quickly and correctly answering questions and completing knowledge-assessing tasks, demonstrate proficiency that indicates the potential for further intellectual enrichment.

Types of Artificial Intelligence to Enhance eLearning

There are a variety of machine learning tools that can personalize the eLearning experience by making courses adaptive to the needs of each individual learner. The below are just a few examples.

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AI Tutor

An AI tutor can be integrated that can allow trainees to freely ask questions at any time. The AI tutor may supply answers by referring back to specific course content, or by referring to relevant online resources. The tutor can even reword material if the student continues to struggle to understand. Individual users can supply feedback on the relative help that the tutor's assistance provides in each instance, supplying the AI tutor with learned experience that can help it make better decisions in its subsequent assistance with future questions.

Personalized Course Content

The eLearning course itself can be pulled from a wider library of materials, with materials selected and ordered based on the individual student profile and past user performance metrics. For example, older and younger students may benefit from different presentations of materials, and the specific mix may adjust over time based on gathered user data. Likewise, how the user performs in terms of the time they take on receiving the course materials, plus the time they take on tests, quizzes, and other knowledge and skills verification tools, can inform how course materials are presented. For materials that are particularly challenging, some redundancy in instruction may be suitable. For those areas in which learners demonstrate immediate proficiency, the topics can be reinforced by encouraging the student to learn the topic in more depth than the average user (AI selection of external source materials, such as relevant news, may be relevant here).

Personalized Knowledge Verification

The eLearning course can adjust its knowledge verification features to focus on the particular topics that are most challenging to each individual user. Tests may pull from a larger pool of questions that are tailored to the unique needs of trainees as determined by their performance in relationship to the past performance of others that exhibit similar strengths and weaknesses with the eLearning course material. Intermittent questions can be introduced depending on the particular student profile and the platform's experience with like users. An AI tutor can start a dialog with the user on topics on which that user is spending a larger amount of time digesting, presenting new angles on the topic to help enhance learning.

Making eLearning Better with AI

The possibilities for using artificial intelligence in eLearning platforms are quite expansive. The opportunity to create eLearning platforms that grow in their capacity to communicate complex topics to diverse individuals in ways that mimic dedicated instructors and tutors will make eLearning platforms worthy of consideration for many subjects that may have once been considered too complex to be handled through online platforms. Being able to combine the standardization and reach of eLearning with the dynamic adaptability of in-person teaching makes the use of AI on eLearning platforms especially powerful and worthy of consideration.

If you are intrigued by the possibility of having such a dynamic, adaptive, and personalized approach to eLearning, you can learn more by contacting eLearning.health today.

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