ML use cases in E-Learning

Introduction

Machine learning and e-learning go hand in hand with the education transformation. E-learning, as a concept, was first introduced in the late nineties, and, since then, it has been an accepted mode of learning in many educational institutions across the globe. In India, however, its adoption was slow until the pandemic acted as a catalyst for transformation in the way education is imparted, though it is yet to become a robust feature of our education system.

1. Personalized learning

Every modern training group is composed of quite diverse individuals, with different backgrounds and knowledge gaps. While some will need to go through the basics, more experienced learners can skip them quickly by answering questionnaires to show their competence. Using AI solutions in eLearning by analyzing the responses helps define individual levels of topic comprehension and carve a personalized learning journey for every student.

2. Adaptive Learning

Some people prefer reading the text, some prefer viewing videos, some learn best while listening to the audio records. AI-driven analytics helps tailor the content every learner consumes to their liking and adjust the course complexity level. Having their training adapted to their liking greatly boosts the motivation and engagement of learners, leading to improved learning outcomes.

3. Chatbots and Virtual assistance

One of the key problems that faculties face in digital teaching is to provide live feedback/support to student queries. By leveraging AI and ML, chatbots can act as virtual assistants and solve real-time queries. This allows a teacher to dedicate more time from admin tasks to actual lesson planning. Many learners struggle to grasp some concepts from the first run. It’s good when the content is pre-recorded, so they can repeat watching the videos or listening to the audios until they sort everything out. But during live webinars and other training sessions many people just don’t ask their questions. AI-powered chatbots solve this challenge too, as the learners can ask as many questions as they want without interrupting the lecturer and get detailed answers as many times as they need.

4. Digital exams and assessment

The lockdown impacted examinations, and in most cases, schools had to either cancel or postpone the exams. Within some time, institutes realized that online examination would be the new normal. AI and ML provides solution to evaluate online exam environments through retinal tracking, environment stimulus tracking and IP tracking. The data generated through such digital examinations combined with the power of machine learning will auto-generate evaluation papers as well as a course of action for each student to help teachers focus on the facilitation part.

5. Advanced Analytics

It’s hard to evaluate learner’s engagement only based on test results. It’s time-consuming and actually tells the instructor quite a little. Quite the contrary, AI-powered analytics can assess the time spent on solving the test, the number of attempts taken, and other performance-related factors. This can help both evaluate the learner’s progress and define any shortcomings in the course material, identify the best ways to improve the content.

6. Instant feedback

Combining all the above leads to an ability to provide personalized real-time feedback for every user. Thus, instead of perceiving such feedback as public intimidation, every learner perceives it at its face value — as an individual development plan, highlighting the strengths and weaknesses of every learner and showing him a roadmap for further improvement.

7. Better resource efficiency

AI-based eLearning solutions require less human effort to manage and maintain the content. When deployed in the cloud, eLearning systems require fewer resources to run. They also ensure better training efficiency, meaning less work time invested in gaining new skills, and learners being able to achieve the business goals faster.