In their recent report, the Wall Street Journal covered the current effects of AI and Machine Learning on education in China. Needless to say, this report shocked us:
China’s achievements in implementing AI and Machine Learning in education are incredible:
there are robots in the classrooms that control a student’s health level
students wear uniforms with trackers to keep them safe while they are on the school territory
the most notable achievement is headbands which determine a student’s concentration level
Teachers, who were interviewed by the WSJ, unanimously support these innovations, saying that the implementation of AI in school education makes students more diligent and improves their academic performance.
Current State of Machine Learning in Education
Machine Learning, as a branch of AI, has seen rapid development over the past few years.
Last year’s report by MarketWatch has revealed that Machine Learning in education will remain one of the top industries to drive investment, with the U.S. and Chine becoming the top key players by 2030. Major companies, like Google and IBM, are getting involved in making school education more progressive and innovative.
EdWeek also reports that AI in education will remain the key focus for the investors, taking the second place after AR/VR technologies:
Image credit: EdWeek
What’s ahead of Machine Learning and school education next year? What benefits should we expect?
Let’s take a look.
Benefits of Machine Learning to Consider in 2020
Next year, education professionals expect the impact of AI and Machine Learning to grow even more, bringing more benefits on top of what educators can already implement in the classroom:
Predicting Career Paths
This year, researchers launched a trend of creating the algorithm based on machine learning that will help students determine their future career paths and college education options.
Occidental College, which is a liberal arts college in California, has launched research to develop a model that will take into consideration each student’s college commitment decision. Based on the student’s interests and academic performance, the system will help them choose a college that matches these factors.
There’s an obvious benefit in it.
High school students often feel the pressure of choosing the right college and the right major to study. “Our recent research shows that only 10–15% of students in the U.S. know, which path to pick after graduating from school”, says Negithan White, a researcher working on a joint project of SupremeDissertations and IsAccurate.
The ultimate goal of this machine learning prediction model is to help students objectively analyze their skills, abilities, and interests, and based on them, pick the right path after graduating.
Education professionals are pointing towards the biased grading system used in schools.
Because there’s no unified grading system, teachers mainly use their own way to grade students, which is often biased.
That’s why education professionals expect AI and Machine Learning to contribute to creating a more precise grading system. In China, about 60,000 schools are testing a paper-grading system that will automatically evaluate the essays of the students.
The benefit of this paper-grading system is that it will precisely evaluate the style, structure, and narrative of the essays without taking into consideration other factors that teachers usually take into account, like overall academic performance or class attendance.
More Personalization in the Classroom
Amazing results have already been achieved in creating a more personalized approach to each student in the classroom with the help of machine learning.
Brain Power, a Boston-based startup, created a technology which uses Google Glass to help students with learning disabilities remain focused in the classroom:
The technology that they’ve created uses Machine Learning to identify the student’s engagement levels and notify the teacher when a particular student needs more attention.
Right now, Brain Power is doing more research on how to improve their technology to add new features to their product. Ultimately, their goal is to bring more equality to the classroom, where every student, who needs special attention, can get it.
Obstacles and Limitations to Consider in 2020
Not everything in the implementation of Machine Learning in education is about benefits and advantages.
Since the AI-based technologies in education are fairly new, there are several obstacles and limitations that education professionals need to consider.
Flaws in Grading System
Since we’ve talked about the demand for a more precise grading system based on Machine Learning, let’s discuss its limitations.
Since this technology is fairly new, it still has a lot of flaws, which teachers should look out for before implementing this grading system in the classroom.
An investigation by Motherboard, a tech platform by Vice, has shown that grading systems based on Machine Learning used by GRE (Graduate Record Examinations) are still susceptible to human bias. Thus, the technology needs further improvements before it can serve the teachers and students well.
The Problem with Data Collection and Transparency
This issue will still remain one of the topical ones in 2020 when it comes to Machine Learning in education.
In the report by WSJ, mentioned at the beginning of the article, a few parents voiced their concern over how the data is collected by the AI-powered headbands, where it is stored, and who has access to it.
The issue of non-protected digital privacy still remains a topical one in education, as the data is collected from millions of students. A seemingly useful technology aimed at helping students do better at school can turn out to be a time bomb. That’s why, more research should be done to secure the private data of both students and teachers, who use devices powered by Machine Learning.
The innovations brought by AI and Machine Learning to Education are exciting. They allow students to get high-quality education based on practical skills rather than on getting general knowledge.
[Related Article: Deep Learning is Not Always the Best Solution in Education]
But while both students and teachers are looking forward to new advancements in machine learning technologies next year, we should remember that AI still has a lot of limitations. Thus, no matter, how useful and exciting this technology can be, we should use it with caution to protect our privacy and to keep education transparent.
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