Machine learning in education: 
10 use cases, examples, and benefits

Machine learning in education: 10 use cases, examples, and benefits

June 23, 2023

Aleksandr Ahramovich
by Aleksandr Ahramovich, Head of AI/ML Center of Excellence
Machine learning in education transforms the learning process and gives new tools for educational institutions to track and improve students' performance and engagement. The ML-enabled personalized approach helps to make education more inclusive, accessible, and engaging.

Let's explore how machine learning developers can improve multiple processes in the educational sector on the most popular use cases and real-life examples. Then, consider the potential challenges of its implementation and see how machine learning experts can solve them.

The role of ML in the educational sector

Machine learning technologies boost students' academic performance. According to McKinsey, 71% of students cited machine learning-powered teaching assistants as positive innovations in their educational process.

Scheme title: Students’ appreciation of learning technologies
*This technology helps me to improve my learning and my grade
Data source: — Machine learning meets higher learning, 2022


predicted worth of the AI in education market by 2032, including ML

Global Market Insights


the ML share of the AI in education market revenue in 2022

Global Market Insights

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ML for education: key functions

A sub-branch of AI technologies used in education, machine learning encompasses a comprehensive array of learning and training tools powered by self-improving deep learning algorithms that can process big data, identify key metrics, recurring patterns, and relations among variables, and therefore understand how such factors influence the core trends and scenarios in this sector.

EdTech service providers, corporate trainers, and educational institutions typically use machine learning in two ways:

Analytical functions

Some ML-based solutions use machine learning algorithms to analyze education-related data and provide valuable insights to enhance the learning experience. These include the recommendation engines of eLearning platforms, adaptive learning applications, and data analytics software for student performance tracking.

Operational functions

Key areas of AI application like natural language processing (NLP) and computer vision rely on machine learning to identify linguistic and visual patterns and autonomously learn how to replicate innate human capabilities, such as sight and communication. These represent the core of interactive tools like virtual tutors, educational robots, and real-time translation software.

10 ML use cases in education

Machine learning has numerous use cases in education and positively affects multiple learning environments, spurring massive interest in the broader adoption of AI-driven technologies.

Adaptive learning

ML-based predictive data analytics helps cater to the unique needs of every student. This technology has been implemented into several eLearning software solutions to monitor various parameters (such as exercise completion time, time spent on the platform, number of logins, and test results) and assess student performance, engagement, and learning patterns, or even forecast learning outcomes.

With the help of ML, adaptive platforms such as MobyMax, SchooLinks, Carnegie Learning's LiveLab, and Kidaptive can adjust individual learning activities in real-time or help teachers and students plan personalized curricula. Similar tools can deliver special educational programs for students affected by specific conditions, such as autism.

Inclusive learning

Machine learning and other artificial intelligence-driven technologies contribute to making inclusive education a reality. Students have various social backgrounds, learning styles, and personalities. Some have learning, speaking, hearing, or other physical or mental disabilities. However, all deserve equal opportunities to study.
ML-driven inclusive education disrupts biases and increases accessibility for a wider audience without compromising education quality. Assistive technologies (AT) and learning systems for disabled or older people grant them access to online education and help overcome learning barriers.

Assistive learning devices and software systems

Input technologies
  • Braille keyboard
  • Eye, head, and foot control systems
  • Sip and puff mouth-controlled joystick 
  • Mouse emulation
  • Single-switch entry devices
  • Speech input/dictation software
Output technologies
  • Refreshable Braille display
  • Screen magnification software
  • Screen reader
Text input improvement software
  • Abbreviation expansion
  • Efficient text entry interface
  • Spelling and grammar check software
  • Word prediction
Assistive listening systems
  • Audio induction loops 
  • FM microphone and receiver systems
  • Infrared (IR) listening systems
AT for blind and partially sighted people
  • Braille embosser 
  • Close circuit television (CCTV)
  • Scanner with optical character recognition (OCR)
  • Tactile diagrams
AT for deaf and hard-of-hearing people
  • Sign language-text conversion software
  • Subtitles (closed captions)

Dropout mitigation

A welcome side effect of predictive analytics applied to education systems is the ability to track specific variables, such as poor school performance and below-average attendance, to identify students at risk of attrition and dropout and proactively set up personalized initiatives aimed at motivating them and filling their knowledge gaps.

Scheme title: ML-based identification of students at risk of attrition
Data source: — Using machine learning to improve student success in higher education, 2022

Real-life phenomena exhibit complex nonlinear patterns

Risk 1Risk 2
Real historical data on prospective candidates, likelihood of attrition
High likelihood
Low likelihood

Machine learning can be used in making the decision to reach out to a particular student at risk of attrition:

  • Student has a 3.2 GPA
  • Student has excellent community engagement and extracurriculars, and has demonstrated leaderchip
  • Excellent attendance and on-time assignment submissions
  • First-generation college student and has some delayed payments

AI tutor and chatbots

Today, we interact with several virtual assistants daily, and students are no exception. NLP-powered tutors and chatbots can perform various educational and administrative functions, such as recommending eLearning content, engaging students with quizzes and training activities, moderating online learning communities, and providing 24/7 user support to streamline student enrollment.

Image title: Juji's cognitive AI assistants
Data source: Juji

learning assistant
teaching assistant

Robot teachers

Chatbots' tangible "colleagues" can bring virtual interactions from digital environments to the real world thanks to computer vision and NLP-powered capabilities. Educational robots are typically implemented for early childhood education to develop creative and social skills, especially among introverted kids. Here’s an example from Singapore:

Task automation

According to McKinsey, educators spend half their working hours on preparation, evaluation, and other purely clerical tasks. Therefore, lack of time is a major obstacle to implementing personalized learning. Fortunately, ML-based software can automate most of these procedures, including test grading. For example, a UK-based company Century Tech offers ML-based solutions for students and teachers that use big data analytics and cognitive neuroscience to improve student performance and reduce teachers' workload, automating monotonous and time-consuming tasks, such as creating learning materials, marking and assignments distribution.

Targeted marketing

Targeted ads represent a step before recommendation systems in guiding prospective students toward a personalized learning experience that best meets their needs. With the same process adopted by ML-powered recommendation systems, data analytics software can peer into user data collected from social media and other sources to identify learners potentially interested in specific services, be it eLearning platforms or private educational institutions, and target them with personalized ads to promote subscriptions and matriculations, respectively.

Here are the results of an ML-based analytical solution adopted by a private nonprofit university and reported by McKinsey:

Scheme title: Conversion rates of an ML-based targeted marketing solution
Data source: — Using machine learning to improve student success in higher education, 2022

Note: Advanced-analytics modeling can predict lead-to-application and application-to-matriculation rates. 1“Uplift” indicates the conversion achieved in the top decile, as ordered by predictive model score, divided by current average conversion rate. 2Average conversion rate based on 2019 client data.

Real-time translation

Breaking down language barriers to broaden the reach of an educational program and promote inclusivity has never been easier, thanks to the availability of real-time NLP-based translation and transcription solutions. For example, tools like Microsoft Azure Cognitive Services Translator and Presentation Translator can translate video lessons, papers, and other types of eLearning content in real-time to make your educational services available to a global audience. Amazon Translate neural machine translation service delivers affordable, high-quality real-time translation and localization services, making the translation more accurate and natural-sounding.

Corporate training platforms

The modern challenges professionals and corporations face, including faster innovation cycles, higher workforce mobility, and a general shortage of specialized talent, foster the adoption of ML-powered EdTech solutions in the workplace. ML-powered corporate eLearning systems can optimize training budget planning, calculate the ROI of your training initiatives, and assess your workforce expertise to generate suitable upskilling plans.

Several online platforms specifically designed for corporate eLearning, such as SAP learning hub, LinkedIn Learning, and AstraZeneca's mentoring portal, have already implemented a mix of ML-based tools.

Fraud detection

Machine learning is widely used for detecting fraudulent activities in online learning environments. ML algorithms can detect cheating patterns by analyzing students' responses, mouse movements, and keystrokes, identifying if the answers were copypasted or if students used virtual assistants. They can easily detect plagiarism by comparing submitted works with other students’ materials and available online sources. ML can also prevent identity theft by verifying the identity of people taking online courses via checking biometric data or facial recognition. For example, Proctorio is an ML-enabled online proctoring tool that detects cheating behaviors during exams, such as copypasting and switching tabs. The app also uses facial recognition to verify students' identity.

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Emerging technologies to pair with ML in education

Machine learning goes hand in hand with other technologies capable of disrupting a traditional educational process and making it interactive, engaging, and accessible for everyone.



Value for education



Value for education

VR creates an immersive digital reality simulation.

Virtual reality transforms the traditional approach to education, offering students an immersive learning environment that is more interactive and engaging. VR in education provides hands-on experience, eliminating some risks and potential dangers associated with practical studies, for example, in flight or driving simulation.



Value for education

AR enhances the physical environment with additional digital content.

Augmented reality overlays digital information, including graphics, videos, and sounds, on top of the real-world environment. With the help of AR, teachers can enable students to grasp abstract concepts better.



Value for education

IoT is a network of connected devices that can gather and exchange information and turn it into actionable insights.

IoT technologies enable schools to efficiently track their resources, improve the safety of their campuses and create new learning environments using smart boards and other connected devices.



Value for education

RPA bots emulate human actions, automating various repetitive and time-consuming tasks.

RPA can streamline various educational processes, from attendance management and processing transcripts to answering queries and automating student feedback and survey analysis.

ML-powered eLearning apps and edTech platforms

Following the rise of eLearning, the education sector has experienced a proliferation of apps and platforms that allow individual users to study at their own pace and learning groups to interact in a highly collaborative virtual environment. Here are a few examples of online tools leveraging machine learning for various functions:

The unconventional educational approach embraced by this app combines digital activities and physical game pieces identified through a computer vision-powered camera. It aims to engage children with an interactive, gamified learning experience covering various subjects, including coding, math, spelling, and drawing.
ML-based learning platform Gradescope is designed for students and teachers. It allows students to assess each other and provide feedback. Meanwhile, teachers can quickly and fairly assess and grade online homework and exam papers and focus on more important tasks like preparing new educational projects all in one place.
With semi-supervised ML algorithms, McGraw-Hill's tutoring and assessment platform can frame students' knowledge and learning patterns based on test results and other parameters. The artificial intelligence-driven system provides users with tutorials and tailored learning modules while offering timely feedback on students’ progress to parents and educators.

Image title: ALEKS student progress report
Data source:

This AWS-based eLearning platform offers a fully personalized learning experience by tapping into Amazon's machine learning-focused services. It also includes features such as smart content search and ML-based fraud detection to supervise content uploading and detect spam and copyright infringements.

Despite being more famous for its cute but peremptory owl mascot/chatbot than for the algorithms driving its operation (including logistic regression and neural networks), this successful language learning app has implemented ML, DL, and NLP to probe user skills with an adaptive placement test, customize each learning path in real-time, provide AI-generated lessons, and fine-tune its courses based on user reports.

Prioritizing reports for all of our courses: Each language direction plotted by how much data we have and how accurate the machine learning system is for it

Image title: Duolingo’s ML-based analysis of user reports to prioritize course improvements
Data source: — How machine learning helps Duolingo prioritize course improvements

This online portal and learning community, designed to help professionals deepen their knowledge of SAP's solutions, relies on machine learning models and NLP to power smart bots acting both as moderators (answering questions, facilitating student onboarding, supervising user interactions, etc.) and curators (recommending suitable training resources).
ML-powered SAP Learning courses

Image title: ML-powered SAP Learning courses
Data source:

Benefits of machine learning in education

Applying machine learning helps to evolve traditional teaching methods, reduce excessive paperwork handled by teachers and administrators, and ensure greater accessibility to education for everyone.

Personalized learning experience

Improved learning outcomes

Enhanced operational efficiency

Increased profitability

Fostered inclusivity

Reduced school dropout rate


through tailored content recommendations, customized learning paths, and adaptive training activities.
via education analytics platforms to track student performance and adjust learning speed and personal curricula.
by automating several clerical duties, such as administrative, moderation, and evaluation tasks, via ML-powered bots.
of your educational services and training initiatives via targeted ads, cost monitoring, course optimization, and ROI analysis.
thanks to NLP-based real-time eLearning content translation and transcription, along with tailored learning pathways for special education.
by identifying at-risk students via learning analytics, assessing their areas of weakness, and designing tailored interventions.
Personalized learning experience
through tailored content recommendations, customized learning paths, and adaptive training activities.

The challenges of ML-driven education

AI-driven technologies, including ML for the education industry, are becoming more and more accessible every day. However, several persistent implementation challenges impede their widespread adoption, especially in socio-economically disadvantaged societies. How to overcome ML adoption barriers?




Privacy and data security



Learning experience enhancements shouldn't come at the expense of personal data protection, which has always been a pain point of any ML-powered application requiring high-volume data sets. To comply with data management regulations ML models are trained with non-personal and obfuscated data.


Obsolete infrastructure



While several embodiments of machine learning in education aim to promote inclusivity, the requirements for hardware availability, data sources, and internet reliability can turn machine learning and EdTech into a source of further inequalities. Before implementing ML-based systems, education institutions should assess the available infrastructure, upgrade it if needed, and ensure they have an adequate budget.


Deployment complications



Educational institutions can also face ML adoption challenges triggered by a general lack of professional expertise, IT infrastructures, and implementation strategies required to readapt teaching methods. ML implementation will require the help of professional AI/ML experts for proper integration, continuous maintenance, and ML model retraining.


Unsupervised machines



Delegating repetitive tasks, such as grading, to ML-fuelled bots without human supervision may lead to unexpected results. For example, the International Baccalaureate implemented an automated scoring system to replace exams in a classroom with data-based evaluations during the COVID-19 lockdown, resulting in numerous anomalous grades and consequent outrage among students. ML software providers should monitor outliers and fine-tune the system to eliminate this risk and ensure algorithms work correctly.

Education 4.0: ML-driven future of learning

AI and machine learning in education drive growth and innovation, just like in any other industry. As cyber-physical systems become ubiquitous in various domains, schools and universities should incorporate emerging technologies into their curriculum to continue producing successful graduates. The upgraded learning process will help students prepare for new professional endeavors. Machine learning in education can reforge the approach to this essential component of personal development and make learning more enticing, productive, and inclusive. And our professional consultants and developers can make the ML adoption journey smooth and effective.

Personalized education
Immediate feedback
Preparing students for evolving industries
Automated administrative tasks
Access for all students

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