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Creating The Next Generation of Adaptive Learning Platforms

  • Published on: April 11, 2024
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  • Updated on: May 19, 2024
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  • Reading Time: 6 mins
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Authored By:

Taranjeet Singh

Director - Program Management

What Is Adaptive Learning Technology?

Everyone’s talking about adaptive learning platforms. So what is it? Well, adaptive learning technology is a roadmap to personalize learning.

We all know learners learn in different ways – each with their unique styles, preferences, and abilities – imagine giving an educator the power to identify these and then cater to each student individually.

How does adaptive learning technology work? For starters, it uses artificial intelligence (AI) and machine learning (ML) to analyze learner data and uses that analysis to tailor content and delivery methods. We’ll get into some specifics later – but for now, take a look at how adaptive learning can help an institution achieve better results.

A boy student using an adaptive learning platform on a personal computer in a classroom.


How Can Adaptive Learning Be Used to Improve Institutional Performance?

Predict learner behavior

Based on eLearning activities, adaptive learning platforms can segment learners based on similarity, and identify learning behavior patterns. This can help measure the similarity between learners and predict their needs, allowing schools to drive timely interventions.

Provide targeted course material

What if we could use these predictions to help a student master a lesson? Adaptive learning technology uses Deep Learning (DL) to analyze learning situations and course material. It uses Bayesian functions to present learners with the most relevant course material. This could help a learner not only receive directly relevant learning material but also receive supporting material to help reinforce concepts.

Identify what’s working (and what’s not)

Adaptive learning technology uses feedback from learning modules to identify what learning paths and objects were most successful in helping a student learn. This data can be used to develop models for similar students. By using these models, schools and educators can help their students reach their learning goals quicker and more effectively.

Build custom learning paths – AI

Using AI/ML algorithms like Q-learning and genetic algorithms, adaptive learning platforms can be trained to build individualized learning paths. These paths would be based on learner interactions, similar learner clusters, assessment data, and feedback from the learner.

Providing real-time assistance through chatbots

Adaptive learning systems often incorporate chatbots or virtual assistants that can provide immediate support and guidance to students. These chatbots can answer questions, provide explanations, offer additional resources, and address any challenges students may encounter, leading to increased engagement and retention.

Focusing on optimal learning activities based on learner profiles

By analyzing learner profiles and behavior, educators can identify the most effective learning activities for each student. This ensures that students are engaged in activities that align with their learning preferences and strengths, leading to improved performance and retention.


Current Challenges With Developing an Adaptive Learning Platform?

If adaptive learning and AI are so great, why isn’t every school catching on? That’s because there are many challenges, given the current learning environment.

Inadequate data

Adaptive learning requires huge repositories of data in order to accurately predict learning paths. Right now, not all learning is digitized and we’re well limited with learning data.

Inadequate expertise

To develop effective adaptive learning platforms, you need experts in AI & ML who can deal with the complexity of combining various machine learning techniques.

Privacy and security concerns

The problem with procuring data for adaptive learning and AI is that you run the risk of infringing on data privacy norms like COPPA and FERPA.  Ensuring data privacy and security while collecting and analyzing student data.


Integrating AI/ML systems with existing e-learning infrastructure can be a challenge because it means upgrades. Schools would need to manage the costs associated with developing, integrating, and maintaining AI-driven systems. ML models also need ongoing training and updates.


What Are the Types of Adaptive Learning Platforms?

There are various types of Adaptive learning platforms. Although advanced adaptive learning systems are in the experimental phase, they are quickly developing. These platforms include various tailored to different educational contexts and goals.

Content-Based Platforms

Content-based platforms focus on delivering personalized content to students based on their knowledge, skills, and learning objectives. These platforms adapt the learning process to the specific requirements of each learner by using algorithms and data-driven insights. By analyzing the learner’s current level of understanding, these platforms adjust the content, difficulty, and pacing of instruction to match the student’s proficiency.

Assessment-Based Learning Platforms

Adaptive assessment platforms help reduce testing anxiety by tailoring tests to an individual’s capabilities. Based on IRT 3PL models, adaptive assessments require a question bank. These question banks are authored using Generative AI. The adaptive assessment platform adapts the difficulty of the questions based on the answers given. This offers a holistic approach to assessment and learning by combining personalized learning and data analytics.

Game-based Learning Platforms

By gamification of the learning process, educators can make learning fun and engaging for the students.  Game-based adaptive learning platforms include interactive games in between modules to sustain student attention as well as sharpen their critical thinking skills.

Comprehensive Learning Platforms

By deploying all the available resources and exploring the full potential of AI, educators can provide a holistic learning experience to students. Comprehensive learning platforms are 360 learning management systems that include all adaptive features for a unique personalized experience.

A smiling young woman sitting at her desk utilizes an adaptive learning platform on her laptop.


What is the Future of Adaptive Learning Systems?

Transparency in AI for Adaptive Learning

As AI and ML algorithms become increasingly integrated into adaptive learning systems, there’s a growing demand for transparency. Educators and learners want to understand how these algorithms make decisions and recommendations. Future developments will focus on designing AI systems that provide clear explanations for their actions, promoting trust, accountability, and ethical use of AI in e-learning. This aligns with the concept of explainable AI, which aims to make AI decision-making processes understandable to humans.

Adding Context to Adaptive Learning

Future directions in adaptive learning involve incorporating contextual information to further personalize the learning experience. This could include data from wearable devices, environmental sensors, or other sources to adapt content based on factors like location, time, or the learner’s emotional state. Context-aware adaptation will enable e-learning systems to provide even more tailored content that is relevant to learners’ specific situations and needs.

Collaborative and Social Learning Components

Adaptive e-learning is increasingly incorporating collaborative and social learning components. AI/ML algorithms can analyze learner interactions, group dynamics, and social network data to provide personalized recommendations for group projects, collaborative activities, and peer feedback. Future developments will focus on leveraging AI to enhance collaboration and foster social interactions in online learning environments.

As institutions focus on taking a more personalized approach to teaching, AI is going to power this pursuit further. As AI integrates the power of AR, VR, and machine learning to provide immersive education and enhance learning outcomes, ensuring ways of equitable access would be the next big challenge. Educators also have the leverage to design dynamic and productive learning environments that can accommodate diverse learning styles and abilities. The infusion of AI in learning also highlights the need for an ethical approach. While AI can help tailor a personalized learning method, the ultimate goal of the technology is to capacitate students to develop independent thinking skills. Platforms must advocate fair AI applications and ensure data privacy.

By focusing on creating a value-based e-learning environment as opposed to mere evaluation and assessments, adaptive learning technology can optimize the personalized learning approach for a broad learner base. Together, the integration of technology and human conscience can transform education for everyone.



Written By:

Taranjeet Singh

Director - Program Management

Taranjeet has over 20 years of experience in the field of education technology. He excels in delivery excellence, growth, and innovation, driving result-oriented solutions across strategy, operations, P&L management, and customer delight.


Adaptive learning technology utilizes AI and ML algorithms to analyze learner data, including learning styles, preferences, and abilities. This enables educators to tailor content and delivery methods to meet individual needs effectively.

Adaptive learning platforms analyze e-learning activities to segment learners based on similarity, identifying behavior patterns to predict their needs and provide timely interventions.

The future of adaptive learning systems involves increasing transparency in AI decision-making processes, incorporating contextual information for further personalization, and fostering collaborative and social learning components. These developments aim to promote trust, accountability, and ethical use of AI in e-learning, enhance the relevance of content to learners' specific situations and needs, and encourage collaboration and social interactions in online learning environments.

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