Machine Learning in Education (Ed Tech) – Benefits and Challenges
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Machine learning in education is redefining the way educational institutions function and impart knowledge and train learners. Young minds are learning about multiple crucial worldly and bodily topics and concepts, using technology. This would shape the mind in a way that would be ready to take the tech-driven world as it comes.
Using machine learning in education has allowed educators to make the learning and training process more personalized and adaptive for the students, among other things. However, some infrastructural ad accessibility issues still make the use of machine learning in education a bit challenging.
Thus, in this blog, we would discuss the role of machine learning in education, its benefits as well as challenges of ML in education.
Learn how AI is transforming education and expanding the possibilities beyond machine learning in our AI in Education
EdTech has seen an increased use of AI and ML in the past few years. In fact, the ML segment accounted for the largest revenue share of over 60.0% in the education sector in 2021This growth was witnessed due to the growth of virtual support in high school, and K-12 schools, driven by rising AI technology investments by educational institutions. This was also required given the benefits of AI in education.
Machine learning in education, along with other technologies, has expanded the impact and reach of education through localization, transcription, text-to-speech, and personalized learning. ML algorithms facilitate continuous learning by autonomously processing new inputs, eliminating the need for iterative manual reprogramming.
It enables the EdTech software to analyze student data and identify behavior, patterns, and trends. These analyses are then used to generate suitable results, recommendations, and suggestions that help the institution offer better learning and teaching experience to the student. As the ML algorithms receive more data over time, they start generating more accurate results, enhancing the productivity and efficiency of the education system gradually.
The role played by machine learning in education involves:
Just like AI, machine learning also brings a lot to the table in the educational sector. It is improving the entire student and educator interaction, with the latter being able to offer more personalized learning through data analytics facilitated by ML technology.
There are some more detailed benefits that come out of this changed interaction. Let’s look at them.
ML programs make use of natural language processing (NLP) and deep learning that translates and transcribes content easily and accurately. This saves learners and educators the costs, time, and efforts of translation while accessing remote learning or eLearning courses. This also encourages an inclusive education system for people with hearing or visual disabilities and those who are international students.
With the help of machine learning, student data is analyzed to identify skill gaps, among other things, and appropriate additional resources, courses, or extra hours, are recommended. Learners can view their metrics, and progress, and work on the weak concepts or topics accordingly. With the help of ML, the courses are more targeted and effective, rather than the one-size-fits-all approach.
Many administrative and management tasks are automated through digitalization in the education sector, freeing up time for educators to utilize for teaching purposes. Moreover, eLearning courses that are ML-based take care of multiple such tasks. These tasks include onboarding, scheduling, providing setting up instructions, monitoring attendance, and grading assignments and tests. This increases the operational efficiency of the entire system.
With the use of machine learning in education, educational institutions are able to offer effective and actionable feedback by analyzing students’ present and historical performance data. This further helps educators to cater to individual needs and offer personalized learning. This is bringing a massive change in the way students approach learning. They are more involved due to higher levels of targeted teaching and training.
Additionally, Additionally, this enables educational institutions to significantly reduce student dropout rates. At-risk students are identified, assessing their weak areas, and recommending the required course of action, all in a timely manner.
The above factors and some additional ones such as better grading systems, deeper research and expanded reach, and higher quality of learning content, all lead to better education experiences and outcomes.
ML-based systems more effectively and comprehensively grade assignments and tests, taking into account plagiarism and internet usage. Furthermore, deep data science capabilities allow better discovery and research depository management.
Through extensive yet targeted advertising and marketing, EdTech institutions are able to attract the most suitable student crowd. It additionally, streamlines the administrative process while considering all the security concerns during enrolment.
Now let’s look at some of the challenges faced by ML in the education sector.
While there are ample benefits offered by machine learning in the education sector, however, the global education sector infrastructure and the internet accessibilities are some issues that are challenging the digital transformation process in the education sector.
Machine learning in education, in particular, also comes with a set of its own challenges. Some of them are:
Data is the most crucial aspect of the machine learning technology. Therefore, the challenges faced around it can hamper the performance of ML algorithms and thus, the quality of results might go down.
Thus, the data addition should be done carefully, with the appropriate amount of time devoted to the data preprocessing process. Suitable ML algorithms such as regression, classification, dimensionality reduction, etc., should be applied to data before modeling.
The educational institution should provide sufficient amounts of data for the model to do its comparisons and carry out its analysis. A larger volume of clean and well-structured data directly enhances the accuracy and depth of the model's analysis.
Additionally, object/ input classification or speech recognition may take more than millions of samples to offer reliable results.
Read Also- Machine Learning In Banking
Machine learning is being increasingly adopted by businesses across various industries. However, it is still in its nascent phase. Thus, there is a shortage of qualified resources or professionals to manage deep learning and analytics.
Since ML technology runs on massive amounts of data, it is more prone to cyberattacks and security breaches. Any ML-based software or app goes through this risk since it has a high-volume of data sets.
Thus, strict data security and privacy measures and provisions have to be put in place by educational institutions to overcome this challenge.
Some popular software that uses ML in the education sector is as follows:
Grammarly needs no introduction. It is one of the most used software around the world. It is a checking and plagiarism detection tool that is used by millions of students and professionals globally every day to better their essays, emails, articles, etc.
The platform uses machine learning, deep learning, and NLP to identify common and repetitive errors such as spelling, grammatical, and punctuation mistakes and improve suggestions in a written piece. It takes users’ behavior, patterns, and actions into account to enhance the literature.
This ML-based learning tool streamlines grading tasks for both students and teachers. Students use Gradescope to access each other’s work and offer feedback, while teachers can assess and grade online assignments and tests by providing the platform with a list of criteria for grading.
This ML-based tool, developed by the University of Michigan, analyses text through automation to enhance students’ writing skills. The ML algorithms, trained on past data, recognize varying levels of knowledge and skills.
Diverse methods are applied to analyze the text and give appropriate results by M-Write. Additionally, the in-build prompts of the program help with the differentiation between weak and strong answers. Peer-to-peer review is a bonus feature of this tool.
There are more such ML-based tools that are enhancing the quality of educational content and learner’s skills such as ALEKS, Duolingo, Echo360, Weka, Orange, etc.
While machine learning is highly beneficial for learners, it also helps educators and institutions to offer their best in an educational setup. Machine learning personalizes the learning process, enabling teachers to offer more targeted and effective feedback and enhancing the entire learning and training experience.
There are certain challenges that the education sector faces while adopting machine learning technology. However, all those challenges could be overcome by careful data preparation before modeling and focusing on data privacy and security. Furthermore, machine learning is still a relatively new technology. Thus, it should be adopted under the guidance of experts or professionals who are well-versed in the concepts of machine and deep learning and data analytics.
BigOhTech is one such expert in the AI/ML field. With years of experience and expertise, we help clients to adopt ML technology effectively, optimizing resources and results.
Our dedicated development team ensures that the data is pre-processed meticulously, and all the right ML algorithms are applied, exactly matching the project needs.
No. Machine learning can help teachers automate tasks like grading, scheduling, and feedback, but it cannot replace their mentorship, emotional support, and judgment. It works as a support tool rather than a substitute for teachers.
The biggest challenge is student data privacy and algorithmic bias. Machine learning models require massive amounts of sensitive student data to function, which increases the risk of data breaches. Additionally, if the historical data used to train these models contains biases, the AI can make unfair predictions or recommendations for certain student groups.
Several education tools use machine learning for tasks such as writing feedback, automated grading, adaptive learning, and personalized instruction. Examples include Grammarly, Gradescope, M-Write, ALEKS, and Duolingo.
It can be, if educational institutions use appropriate data security and privacy measures. Access to student data should be limited to authorized users, and institutions should follow applicable privacy requirements and security practices.
Machine learning analyzes student performance and learning patterns to identify skill gaps and recommend relevant content, practice, or feedback. This helps educators provide more targeted learning experiences instead of using the same approach for every student.
AI is the broader concept of using technology to perform tasks that typically require human intelligence. Machine learning is a subset of AI that learns patterns from data to make predictions, recommendations, or decisions.
Machine learning can analyze student performance, behaviour, and learning patterns to recommend relevant content, courses, and interventions. This allows institutions to move beyond one-size-fits-all learning and create more adaptive experiences.
High-value use cases include personalized learning, skill-gap identification, predictive analytics, automated grading, student-risk detection, feedback generation, and administrative automation. The right starting point depends on where your institution has the biggest operational or learning bottleneck.
ML implementation for an EdTech platform usually runs $5,000 to $150,000+, and most mid-tier builds land between $20,000 and $100,000. Where you fall depends on whether you're plugging into ready-made APIs or building a custom predictive system from the ground up.
Simple stuff like doubt-solving chatbots or quiz grading costs $5,000–$20,000 and ships in weeks. Adaptive learning paths and behavior analysis sit in the $20,000–$60,000 range. Custom virtual tutors or real-time predictive analytics can run $60,000–$150,000+ and take several months.
If your requirements are standard, an existing platform can be faster and more economical. Custom ML becomes more attractive when you need proprietary student insights, specialized personalization, unique workflows, or deeper integration with your existing education ecosystem.
Yes. ML capabilities can be integrated with LMS, student information systems, assessment platforms, content repositories, and other institutional systems through APIs and data pipelines. The key is to assess your existing architecture and data accessibility before selecting the ML approach.
Yes, ML can support automated assessment and feedback, particularly where evaluation criteria are structured and sufficient training data is available. However, for high-stakes or subjective assessments, keeping educators involved in review and validation can help maintain accuracy and fairness.
ML can automate repetitive processes such as onboarding, scheduling, attendance monitoring, grading, and other administrative activities. This allows educators and staff to spend more time on teaching, mentoring, and higher-value work.
You need enough relevant, reliable, and properly structured data for the intended use case. Depending on the application, this could include student performance, assessment results, attendance, learning activity, course interactions, or historical outcomes. Data quality is particularly important because poor-quality data can directly reduce prediction accuracy.
That depends on the use case, data volume, quality, consistency, and historical coverage. An ML assessment should be performed before development to determine whether your current datasets are sufficient or whether additional data collection and preprocessing are required.
Evaluate the business problem first, followed by data readiness, infrastructure, integration requirements, privacy, security, available expertise, and expected ROI. Starting with a clearly defined use case is generally more practical than implementing ML simply because the technology is available.
Start with a focused proof of concept around one measurable problem, such as skill-gap detection, student-risk prediction, personalized recommendations, or administrative automation. Once the model demonstrates measurable value, you can scale it across additional courses, users, or workflows.
An experienced ML development partner can help assess your use case and data readiness, select appropriate algorithms, prepare the data, build and integrate the solution, and support deployment. This can be particularly useful when your internal team lacks specialized ML and data-science expertise.
Building an ML solution for EdTech takes anywhere from 3 to 9 months, depending on scope. Simple tools like FAQ chatbots take 2 to 4 months, while full adaptive learning platforms with predictive analytics can stretch past a year.
Most of that time goes into data prep and model training, not just coding. And if you're handling student data, factor in extra weeks for privacy compliance like FERPA or GDPR before you can even think about launch.
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