Machine learning in education: 10 use cases, examples, and benefits
June 23, 2023
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.
$30B
predicted worth of the AI in education market by 2032, including ML
Global Market Insights
20%
the ML share of the AI in education market revenue in 2022
Global Market Insights
Reinvent education with Itransition’s machine learning solutions
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.
Transform traditional education with ML
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.
Technology/Description
Technology
Value for education
Description
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.
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.
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.
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.
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
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).
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
Benefits
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?
Challenge
Solution
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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