The Future of Machine Learning in Education: List of Inspiring Applications

Machine learning has become an integral part of multiple industries. From autonomous vehicles to e-commerce stores, machine learning finds applications in nearly every aspect of our daily lives. 

However, when we talk about machine learning, an industry that rarely comes to mind is education which begs the question, “Are there any applications of machine learning in the education sector?”

As it turns out, there are plenty of applications of machine learning technology in education. This article will share some of the most prominent ML technology applications in teaching and education and show how bright the future of these two is. 

Before we start talking about machine learning and education’s relationship, let’s first discuss the technology itself. 

A Brief Introduction To Machine Learning

In machine learning, you create machines that can execute tasks and learn from them without requiring any human intervention. 

What does it mean? 

It means the machine doesn’t require you to enter the task every time you use it or make changes to its operation. The machine will learn to better its performance with each task and implement the necessary changes in the next iteration.

Sounds fascinating. 

The education sector isn’t the only area where we use machine learning. It has a ton of applications in our daily lives. The face recognition lock on your iPhone uses machine learning to identify your face. 

Similarly, Google Assistant learns every time you use it to give you a better experience. When a spam email gets filtered out automatically in your Gmail account, you can thank machine learning for it. 

Other prominent industries that use machine learning are manufacturing, transport, finance, healthcare, and many others. 

Applications of Machine Learning in Education

The education and e-learning industries can benefit highly from incorporating machine learning and artificial intelligence. Following are some of the primary areas of education that can benefit from the use of machine learning: 

Reduced Bias in Grading

Machine learning can help teachers in examining student assessments and assignments. They can determine if there is any plagiarism and find other similar errors. Machine learning tools can grade students and provide suggestions on improving the grade, making the teacher’s job much easier.

Moreover, machine learning implementations can reduce bias in grading, which can be a considerable flaw. A teacher’s attitude towards a student shouldn’t affect the grades they allot to students. An ML framework designed to evaluate students would perform grading unbiasedly, solely based on their performance. However, that doesn’t mean they wouldn’t need human intervention.

The educator would still have the final say as they can keep other factors in consideration, such as the student’s behaviour and their in-class participation. 

Machine learning grading/evaluation applications would make the grading process much efficient and easier to manage. This would allow educators to shift their focus on other crucial areas of teaching, which leads us to our next point. 

More Efficient Operations

A big reason why artificial intelligence and machine learning have become so popular is they allow organisations to automate operations. Automation increases operation efficiency substantially. 

E-learning companies and educational institutes can use ML to automate their day-to-day tasks and optimise their operations. They can use virtual assistants to help students find relevant courses and study material much quickly. Similarly, they can automate daily tasks such as storing student-related data and scheduling by using ML tools.

According to MIT (Massachusetts Institute of Technology), more than 96% of MOC (Massive Online Courses) students give up their courses. Using ML can help organisations enhance their learning experience and rectify this issue. 

Career Path Prediction

Another prominent application of machine learning in education is career path prediction. Predictive analysis is a core component of machine learning, where we use ML algorithms to predict an outcome accurately.

You can train ML algorithms to take input from students and chart out customised career paths for them. They can study the data gained from teachers and parents to get more insight into an individual student’s interests and career aspirations. 

They can use personality tests and IQ tests to help generate career paths for students, allowing them to find careers they will excel in and enjoy. The technology can also predict students’ problem areas and assist them, such as extra classes or workshops, to succeed professionally.

Such machine learning implementation will allow students to get rid of career-related confusion and make better-informed decisions about their profession. Students will be able to identify their strengths and maximise their potential. Similarly, they can find their weaknesses early and strengthen those areas with optimal performance. 

Enhanced Learning Experience

Every student is unique in that each grasps concepts differently, at a different pace. Incorporating machine learning can help institutes and e-learning providers to offer better and more personalised learning experiences to their students.

ML can allow you to develop detailed logs for every student, providing them with learning material based on their specific interests and requirements. It can help educators understand how well each student understands different concepts. 

They can use this information to customise the study material and plans for each student, allowing them to learn steadily and effectively.  

Artificial Intelligence and Machine Learning can help students get personalised courses based on their exact requests. This can save a lot of time and make the learning experience highly efficient. 

Recommender systems are a prominent application of machine learning and AI. They focus on giving personalised recommendations to a user, depending on the user’s interests and history. E-learning providers can use recommender systems to suggest courses that match a user’s interests and requirements. Many major companies use recommender systems such as Amazon and Netflix, which allow them to give a better user experience to their customers.

Recommender systems in E-learning will make it easier for people to find courses for their career aspirations and interests. 

How Is The Future of Machine Learning In Education? 

Machine learning can solve many problems in the education sector. It can simplify a teacher’s job, reduce stress, and enable them to offer more personalised learning experiences to their students. 

Some educational institutes and companies have started using ML already. For example, Cram101 is a service that uses ML to create study guides and chapter summaries of textbooks to make them easy to understand. 

Another prominent solution is Netex Learning, which allows education institutes to create curriculums and integrate video and audio with their study material. 

Many organisations have started implementing ML technologies in innovative ways. Thus, rest assured, you can certainly expect to have a future-proof career in Machine Learning.

Moreover, a machine learning engineer’s average salary is $112,852, so it’s undoubtedly a very lucrative career. If you’re interested in a career in education, you can enter as an ML expert.

What do you think about the future of machine learning in education? What other impacts can it have on this field? Share your thoughts by dropping a comment below. 

With all the learnt skills you can get active on other competitive platforms as well to test your skills and get even more hands-on. If you are interested to learn more about the course, check out the page of Executive PG Programme in Machine Learning & AI and talk to our career counsellor for more information.

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