Machine Learning in Higher Education: Benefits and Obstacles

Machine Learning (ML) has had an impressive effect on technological progress so far. It’s an invaluable tool designed to create models and export valuable and highly practical information from massive data sets. The most impressive part of it all is that we’ve just scratched the surface in regards to what ML has to offer. Furthermore, we haven’t yet started integrating Machine Learning into the fields of human activity that can considerably benefit from it, like education. 

In this article, we’ve compiled a set of essential benefits that ML can bring to education in general and some obstacles associated with it. As the human activity becomes more quantified and digitized. The datasets we’ll have access to and extract important patterns from will be continually growing, allowing us to optimize our professional and academic performance.

ML and its potential impact on education

Considering that Machine Learning is at its core data mining, the first application that comes to mind is tailoring personalized courses for students that have specific needs, interests, based on the insight extracted from their performance and background. This could allow higher education to have a much more granular approach towards training highly-qualified professionals. 

Nowadays, most universities are packed with students, which makes it very complicated to have an individual approach towards each student, this, however, is one of the most prominent factors for high academic performance.

Eliminating bias from grading

This has been a serious issue, throughout the entire history of education. In regards to appreciating someone’s intellectual or academic performance, bias isn’t even a point of contention anymore. The scientific community recognized that there’s always an element of subjectivity to our judgement, which is why we use double-blind peer reviews to approve someone’s scientific input. 

The impressive benefit that ML brings to the table is an automatic grading system that is objective and unbiased. This allows professors to focus on what they do best and not worry that their decision to give a specific grade to a particular student could have been influenced by their relationship with the student or vice versa. Refraining from human-based grading may allow us to create a more meritocratic environment in colleges and universities around the world. 

Setting the right expectations

One of the central responsibilities of a higher-education institution is to establish reasonable standards when it comes to student performance. By continuously analyzing the data on how students have performed, colleges can always make necessary changes to the curriculum and fine-tune it to the current market needs and the students’ capacities. 

Similarly, this aspect can be partly intertwined with the benefit of personalized education. Considering that ML could be able to show the students that are likely to fail at certain tests, which would help decrease the fail rate in the institution. 

Kansas City has implemented a complex ML mechanism that can identify pothole formation even before those start forming. This is an excellent metaphor for how universities can harness the tremendous computational power of ML to eliminate poor academic success.

If approached correctly, this would allow universities to have a more meticulous approach towards enhancing the students’ academic performance. Which would also improve the overall quality of higher education across the board. 

There are also many tools that have come into existence almost exclusively due to Machine Learning — Grammarly is a famous example. 

Adequately grouping students and professors

This could be a slightly more daring approach to the infrastructure of education. Which implies assigning students to specific professors not just in accordance with their particular needs, compatibility, availability, and a broad spectrum of other complex factors. 

A substantial number of people have had unpleasant and demoralizing experiences with teachers that simply weren’t compatible with their personality. Such instances can often demotivate students from pursuing subjects that they may otherwise find appealing. 

Prioritizing human-specific tasks

Machine Learning will considerably facilitate blended education, which infers the fruitful collaboration of humans and machines in the classroom. Not only does this type of cooperation improve the quality of education for students by making it more efficient via computer-assisted education, but professors can also benefit from it greatly too. 

A curriculum differentiation for machines and professors will allow teachers to focus on things that are inherently human — emotions, since education is deeply rooted in empathy, a sentiment that is yet out of reach for an artificial “mind.”

Potential issues and obstacles

There is a wide range of issues that need to be taken into account when analyzing the benefits of Machine Learning — its limitations and potential ethical issues. ML is an incredibly powerful tool, which needs to be used with caution. Especially at the very beginning of ML integration into the education system, we’ll have to careful not to let our guard down out of excitement. 


ML is now being implemented in a host of different fields. Occasionally, the results were “uncomfortable.” To a certain degree, at the very essence of Machine Learning lies discrimination. Its design implies that it will bring up regularities and irregularities, without taking more emotional factors into account. As a civilization, we have established many rules and standards in regards to how we organized society. Which aren’t exactly part of the “real world.” Human rights or equality isn’t a quantifiable aspect of reality, it’s too abstract for a computer to perceive. 

In today’s society, we refrain from making assumptions about people based on their appearance or other factors that are out of their reach, computers can’t “understand” such concepts. 

There have been some very controversial studies published recently. Which inferred that it is possible to suggest that a person is more likely to commit a criminal act based exclusively on still face pictures. By using reasonably simple ML techniques. Very often, these results may compel us to make poor decisions that are exactly in line with the moral and ethical standards of the society we live in and strive to create for ourselves. 

Another famous study conducted at Stanford University has reported on the ability of a trained AI to successfully identify a person’s sexual orientation. Based on their facial features at very high accuracy. What if ML would be implemented to identify a said student’s political views, IQ, sexual orientation, and even proneness to criminal acts based on their appearance? Wouldn’t that create leverage for the institution’s administration to discriminate against individual students? 


The above-mentioned issues aren’t problematic just from an ethical standpoint, it has to do with privacy as well. Imagine being a person that isn’t comfortable with disclosing a said aspect of their personality, like sexual orientation. Merely appearing in the gaze of a security camera. Whose signal is analyzed by a Machine Learning mechanism that could unveil much more information about you that you’d like to make public. And while this data won’t be in open access, there will still be people that interact with this data.  


ML has an impressive potential to change the way we perceive education as a whole and disrupt the way we used to teach throughout millennia. There are enormous benefits to it, and we’ll most certainly see a dramatic shift in the quality of education. 

It will allow students to learn more efficiently, due to the fact that their needs will be met at a much greater extent than they are today. Their potential weaknesses will be identified before they become an actual problem. Professors will be able to focus on teaching and will not have to deal with grading students. Which eliminates a considerable amount of bias from education. 

With these benefits, however, we do still have to find answers to important questions that relate to the students’ privacy. And the ethical implications of subjecting their performance to a thorough analysis.

About the guest author:

Estelle Liotard is a seasoned content writer and a blogger, with years of experience in different fields of marketing. She is a senior writer and content editor at Top Writers Review and loves every second of it. Her passion is teaching people how to overcome digital marketing obstacles and help businesses communicate their messages to their customers.

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