How Machine Learning Is Changing the Education Landscape

How Machine Learning Is Changing the Education Landscape

'How Machine Learning Is Changing the Education Landscape'

Education ' it's critical for success, but it is not without its flaws. Most of us remember going through high school and having little to no say in what we learned. Entering college typically provided more control, but you were still limited to the defined learning paths set by faculty members.

That's changing, thanks to the intervention of machine learning in the education sector. Machine learning in education opens incredible new possibilities and opportunities for students to learn, develop, and grow.

Machine Learning and Education

AI and machine learning in education have already created major impacts. You can see this in several aspects, including user-friendly machine learning applications, as well as the implementation of capabilities designed to improve learner outcomes and experiences, such as:

Improved Personalization ' Machine learning provides expertly tailored suggestions to help customize a student's learning experience, both within professor-led learning and self-paced studies.

Time Use ' Once trailed, machine learning algorithms require virtually no human interaction and can automatically complete tasks, including learning analytics, plagiarism checks, grading, and more.

Modern Analysis Technique ' Machine learning can perform virtually all analyses required within an academic setting. Professors can use that information to adapt the curriculum for specific student needs, as well.

Reduced Errors ' Human error is the primary cause of issues within an education setting. Machines perform monotonous tasks better than humans, with virtually no errors. This reduces time and costs while delivering improved outcomes overall.

However, those are just the tip of the proverbial iceberg. Machine learning also enables the development and implementation of different learning models that empower better outcomes for students. These adaptive learning programs automatically analyze a student's capabilities, preferences, and other considerations and then adjust to match their needs and educational goals.

Two other important benefits of integrating machine learning in education are cost reductions and improved access to customized curricula. This provides learners with access to personalized curricula based on their interests and career paths. They're also able to access learning materials 24 hours per day, 365 days per year.

Adaptive learning will also be instrumental in closing the gap created between educators and students during the COVID-19 pandemic. By adopting a multi-channel approach empowered by artificial intelligence and machine learning, professors can easily leverage the best of digital learning while managing a remote classroom. It becomes simpler for professors to monitor student attendance, engagement, and involvement.

Digitalization and Analytics: Understanding the Benefits of Machine Learning in Education

The adaptive learning model offers a broad range of benefits that help to offset the many challenges in distance education. These include:

Learning Analytics: Learning analytics can help determine which courses are best for specific learners based on their underlying needs, interests, and career goals. That data can then be used to tailor future educational content to deliver a better learning experience.

Interaction and Connection: Machine learning helps close the gap between students and professors within distance learning environments. It also makes it easier to interact with one another and track student progress through the learning material. For instance, it enables virtual assistant professors to interact with students through applications and websites. Students simply type a query or provide input, and the assistant can determine the appropriate response based on aggregated information about the student, including their preferences, learning history, and more. Another example is the use of machine learning analytics to assess a student's progress through study material, identifying areas where the student might progress more slowly or not at all. The professor or an assistant can then proactively reach out to address the situation or to provide customized research or learning materials specific to the student.

New Fields in Education: Machine learning opens new fields in education by estimating learner and educator requirements and accessibility. It enables the analysis and forecasting of students' academic success and can also improve the accuracy of student grading. It allows professors and other educators to structure learning material correctly for students while achieving greater stickiness and information retention. For instance, machine learning analytics can improve retention by identifying and then reaching out to 'at-risk' students to provide them with the resources they need to succeed.

Cost Reduction: Predictive analytics offers the most cost-effective method for universities to save money by identifying those students most likely to struggle ahead of time. This allows universities to proactively address learning-related issues, reducing time and money-related costs due to students repeating courses.

How Machine Learning Is Transforming Professor-Student Relations

Machine learning is radically transforming the student-professor relationship, as well as the student-education paradigm. Below, we discuss some of the most important ways ML is accelerating the education industry's evolution.

In-Depth Knowledge

Machine learning can develop in-depth knowledge by using real-world examples and overcome a major pain point for both students and professors. For instance, developers teach machine learning models to correctly identify images that are either original or fake photographs. This can be an added advantage in terms of security, as well This method can also help visually impaired students using screen-reader technology to understand what is being taught. Screen readers are essential tools for people with low or no vision, who have trouble reading, and those who are color blind by converting text into synthesized speech. Screen reader technology can also include Braille output so learners can read text by touch.

Always On

Students and educators can access learning material at any time of day or night from any location with an Internet connection. eLearning platforms are also mobile-friendly, ensuring that students can learn via their smartphone or tablet. This can help connect students and professors in different geographic areas, but it also benefits those with unique scheduling needs, such as students who must work during the day when traditional in-person classes occur.


Gamification is popular in military training, corporate training, and in other learning methods. According to Zippia, 95% of employees prefer a gamified work experience, and 30% of them state that gaming-based learning is more engaging. Over 70% of people report that gamification encourages them to do more tasks. Educational games make a natural implementation point for gamification. Students are motivated to learn more and perform better, but machine learning within this setting also tracks how much students learn. AI-powered machine learning and gamification in the classroom have improved teaching and learning, but not self-motivation and engagement among students.


When we think of education, most people picture a student, a professor, or maybe a tutor. That's because education traditionally comes from someone else. But machine learning, natural language processing, and learning analytics allow us to transcend that and create chatbots that can respond to students' questions with accurate answers. That helps them learn while freeing educators for other requirements. Combining chatbots with gamified learning material and other advanced technology makes it possible to create a comprehensive, customized solution that supports learners' needs and improves their outcomes.

While there is concern that chatbots will eventually replace human professors, the truth is that they only take over repetitive tasks, freeing educators to focus on what they do best and making teaching more meaningful. For instance, instead of educators having to memorize lessons or spend hours grading tests, chatbots can handle those repetitive tasks, freeing professors to build stronger relationships with their students through personal guidance and mentoring. That's beneficial for all students, but particularly for those with learning disabilities and other challenges.

Fast and Consistent

Another benefit of machine learning in education is that it can be accessed quickly and at high speed without interruptions. Personalization improves learning and tracking performance according to a student's current grade level and curricula, like test and quiz scores, homework completion rates, and more.

For instance, personalized learning allows students to learn at their own pace, set their own schedules, and balance their personal and academic lives. They are no longer forced to sacrifice careers or personal enjoyment to build skills or earn a degree. Machine learning will improve student assessment procedures, as well, through automatic grading to save teachers time and reduce potential bias.

Machine learning helps educators forecast enrollment and meet demand, too. Predictive maintenance can help institutions manage their facilities and equipment better, and machine learning empowers students and staff to control data better and prevent fraud.

AI and machine learning within higher education also improve research and discovery, with a wide range of possible applications, including:

Detecting paraphrased content, plagiarism, and image modification.

Predicting high-impact research and emerging areas of study.

Augmenting the peer review process through automated or semi-automated content creation based on human input.

Digital Blackboard

Machine learning, AI, and eLearning combine to create what amounts to a digital blackboard. That is a central location for everything that a student might need, with each access to assignments, homework, announcements, course content, communications from instructors, and more.

Analytics of Cheating

Cheating occurs in in-person learning, but many assume that the incidence is much higher in eLearning environments where the educator cannot directly monitor student activities. Machine learning and AI can use facial and voice recognition technology with test-takers, similar to having a professor present, but more reliably and on a larger scale than human beings can achieve. It's also possible to match background noise to speech patterns to detect falsification and even detect eye movements that might indicate unauthorized reading.

Attendance-Based Analytics

One of the key challenges distance learning educators faced during the pandemic was attendance. It was very challenging for professors to ensure that all students were present for classes. Machine learning and AI provide accurate attendance-based analytics to help reduce this problem and ensure that every student has ample opportunities to learn. Evaluating attendance is important for colleges and universities. Traditional attendance recording involves calling out roll numbers or student names and can take between five and 10 minutes out of a 60-minute class. With AI and machine learning, image processing takes virtually no time at all. And, because the professor is not involved in attendance taking, they're free to move on with the course material.

Individual Analytics

Education is an individual journey through the collective knowledge of human beings. In the past, judging progress through that journey has been based not on the individual but on standards developed to suit groups. The problem here is that students can have needs that drastically vary from one to another, making group-based metrics inaccurate at best. With machine learning, it becomes possible to generate individual analytics. These can be used to assess that student's progress, strong points, areas of opportunity, and more, and then create a customized strategy that will benefit that specific student. A good example is ALEKS, an online learning platform powered by AI and machine learning. It generates assignments and homework depending on students' needs and weak areas.

Pass/Fail Analytics

Colleges and universities prioritize student success, which is denoted in pass/fail ratios. With budget cuts, increasing operating costs, and other factors affecting financial stability, universities must maintain their academic rigor and quality as well as increase student enrollment. Machine learning and AI can predict future performance, classify students based on their academic history and in-class behavior, and more.

In Conclusion

Machine learning is no longer something reserved for big tech firms. It's making inroads into the daily experience of everyday Americans. As it becomes more efficient and integrated within the education industry, it's important to understand how it will continue to drive the evolution of teaching and learning. What we can expect is this: an increasingly close alignment between AI, machine learning, and analytics. This will drive changes to the education system that make it easier to achieve positive outcomes within remote learning in higher education environments. The creation of personalized learning paths based on interests, innate talents, opportunities for improvement, and future career paths will ensure a more positive outcome for students and relieve educators of a great deal of stress.

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