Machine Learning in Education 

There is no doubt that artificial intelligence will revolutionize the future of education systems. Its significant potential is expected to have a tremendous impact on the education sector. However, the transition between old-school education teachers using pen and paper to machine learning capabilities is proving to be very difficult. Whilst the world prepares for a changing future with a digital outlook, there is an argument that the presence of teachers will be irreplaceable if children were to rely on robots and machines to assist them with their education. 

It’s predicted that whilst artificial intelligence becomes mainstream, it will create double the amount of jobs that may be lost in the short-term. Schools are now preparing and altering tools and procedures to cater for machine learning activity, where teachers that today take lectures, mark papers and instruct students are expected to be handled by machines. 

There are many ways in which machine learning will revolutionize the future of education with tasks performed by machines. Whilst the transition will be enormous and complex, it is yet to be predicted whether children will receive better productivity through machines or teachers. 

Here’s are the potential ways in which machine learning could transform the education sector. 

  1. Predictive Learning Analytics – Machines can accumulate data to make decisions 

for students using predictive analytics. AI is expected to be efficient to gain insight and output decisions that may not be possible for humans to do. An example is based on the accumulation of student grades, a machine may be able to predict whether students have passed or failed, and even predict their next education path from there on. Predictiveanalytics may have a big role to play in this judgment. 

  1. Improve Class Efficiency and Organization – Machine Learning AI is expected to 

be efficient in developing class programs and learning tasks than humans. Not only this, but general class management including scheduling of classes and setting student tasks. Teachers may then focus their responsibilities on up-skilling and improving student learning. 

  1. Accurate Assessment Grading – Machine Learning can determine accurate 

grading of examinations or assessments. As the machine is fed data, it can determine grading through the standard algorithms that help them become reliable in understanding grading criteria to determine grading results. As time goes on, this will require no human interference. 

  1. Personalized Learning – Maximising student potential can be enhanced through 

personalized learning. A machine can develop a personal education experience that focuses on the needs of the student such as helping to achieve high grades, learning at their own pace and help them make decisions. The curriculum may then be set based on the student input. 

  1. Adaptive Learning to Student Qualities and Challenges – Adaptive learning can 

      help to support struggling students and challenge the clever ones. This can be considered 

      as an online e-learning portal that can monitor student performance and update curriculum

      activities based on their progression. This helps promote better student engagement. 

It’s well known that machine learning is transforming the way we live and work. Whilst it may not have become a reality in the education sector just yet, the opportunities it provides as it enters will become enormous. As soon as it is implemented in the future of education, there 

will be better efficiency in managing students entirely from enrolment through to grading. Whilst teachers may be concerned by their future roles, it is thought that their role may change instead of being removed altogether. 

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