AI based Adaptive Learning

Adaptive learning is defined as a teaching approach in which students receive personalized instruction. In adaptive learning, the main premise is that a tool or system would be able to adapt itself according to a students/users learning methods, resulting in a better, more efficient learning experience for the user. Adaptive learning systems have been used in the past, e.g., to assist students to improve the argumentative writing ability (Argument Mining).

Machine learning techniques could be used for education data mining and prediction of students learning outcomes. For instance, predictive models can predict students grades, and descriptive models can uncover new teaching guides from large amounts of education data. Data analytics informed by adaptive AI solutions can help to detect key areas of student and teacher effectiveness. Artificial intelligence (AI) technologies are already being applied to different learning and/or teaching platforms, and they are going to transform the way teachers teach and students learn.

In the past few years, more advanced AI-enabled learning systems have emerged that are gaining attention for their capability of providing content for learning as well as tailoring it according to individual needs of students. More advanced AI-enabled learning systems. Companies such as the following are now merging the organic and artificial, applying deep learning systems to revolutionize how humans are taught. In essence, most cutting-edge training technologies are moving towards the extreme ends of the spectrum, leveraging cutting-edge AI to promote adaptability. AI-powered platforms are not only fulfilling the promise of smart, instructor-like adaptive learning, but also solving some of the older weaknesses that were built into adaptive systems from the Adaptive 1.0 and 2.0.

As the technology has advanced over the decades, adaptive learning has evolved: it is becoming more sophisticated and efficient thanks to the jumps in cloud-based managed services, computing power, scale, and machine learning. Adaptive Machine Learning brings together the best features from all generations of ML, and it is continuously learning. The adaptive learning engine framework is designed to study and discover each childrens learning patterns from the students responses to real-time quizzes, corrections, and interventions. The adaptive algorithm is capable of discovering a students strengths and weaknesses in order to ensure an optimal learning environment.

The algorithmic adaptation is based on what the algorithm, operating with both individual data from the students as well as aggregate data, determines is most appropriate. Just to clarify, adaptive learning is related to more than education, like knowing a user and optimizing what is offered to them according to their preferences. Embibe is quite similar to the above-mentioned two apps, because it too uses diagnostic tests, analyses a students current levels, and helps them correct their learning gaps. The platformas pedagogy, the adaptive learning engine, is student-centered, with instruction focused on the unique learning needs, interests, desires, or cultural backgrounds of each learner.

Adaptive learning, also known as adaptive teaching, is an education approach that uses computer algorithms, along with artificial intelligence, to organize interactions with a learner and provide personalized resources and learning activities that meet each learners unique needs. Through various techniques, adaptive learning technologies establish a baseline evaluation of each individuals knowledge, then deliver the next training which is personalized for the needs of each learner. Computers tailor the delivery of learning materials based on students learning needs, indicated by their responses to questions, problems, and experiences. Such instruction is personalized for each learner according to his or her preferred style of learning, proficiency, route, and environment.

In AL, learners begin with exams & quizzes, and depending on how well a learner performs, the system adjusts itself based on a learners psychometrics and learning patterns. AL uses the learning metrics and the learners cognitive skills & capabilities, thus, the systems are able to rationalize content to the learner and propose the best module for them to follow for the purpose of achieving focused approach of learning.

Mentee Education & Factr.in Both are using various algorithms and trends models to understand users better and serve them a customized learning experience. When students have fully mastery over all recommended content by our AI-enabled answers, they respond (next) and a personalized learning engine platform sends the students new quizzes which are easier for students, with additional explanations, helping students to get an advanced understanding of a subject. The Adaptive learning engine platform has also been trained to combine simple examples and recommendations with solutions for missing questions and share them with the learner.

The original systems, which did not use adaptive learning, were capable of providing automated feedback to students presented questions from the preset bank of questions. Quizlet Learn uses machine learning and data from millions of learning sessions to present students with the most relevant learning materials. Quizlet recently introduced Quizlet Learn, an intelligent learning resource that provides adaptive plans and helps eliminate guesswork in studying.

We are also building time-motion models and neural networks for student learning, so that our technology can better understand how users are learning, and, in turn, deliver better-informed processes, recommendations, and predictions. The latest generations of remote teaching systems consider the students responses and adjust to a students cognitive abilities using a concept called cognitive scaffolding.

The Adaptive 3.0 platform provides real-time, dynamic content adaptations based on student performance and behaviors, eliminating the need for prior assessments and preset learning pathways. Adaptive learning is about providing learning experiences that fit a students unique needs - be it via adaptive pathways (different students progress through the material differently), adaptive feedback (which is tailored to what a student is doing and needs), or adaptive content (content changes according to what each student needs).

For some, this might seem like we have been talking about adaptive learning forever, and yet we are still seeing plenty of untapped opportunities to consider ways that we can enhance how we use adaptive technologies to engage learners, ignite passion, promote persistence, and support content mastery. AI can provide students with an avenue for experimentation and learning in relatively judgement-free environments, particularly if the AI mentors are available to suggest solutions to improvement.