Ed is a technical content writer with more than seven years of experience. He loves crafting compelling content that explains complex technology in ways that make it easy to understand.
Machine Learning in Education: How ML Is Empowering The Industry
Machine learning (ML) is used in the development of predictive AI and generative AI models that help a myriad of industries.
When it comes to education, ML offers invaluable benefits: from personalizing learning paths to tracking student performance to automating administrative tasks to identifying at-risk students. Machine learning algorithms transform traditional education, helping organizations streamline educational processes and deliver a better learning experience.
As a company that specializes in educational software development services, AnyforSoft would like to discuss the role of machine learning in education. In the following paragraphs, we will talk about how ML models are empowering the education industry while demonstrating the applications of the technology.
Without further ado, let’s get started!
Use case #1: personalized learning
Personalized learning experience is arguably the most significant advantage that machine learning and artificial intelligence bring to the education sector.
As discussed in our article about the future of EdTech, it’s challenging to personalize the educational process in a traditional learning environment. A teacher can’t physically pay attention to every student in their class while recognizing their preferences, needs, interests, strengths, and weaknesses.
With machine learning, however, the situation is different.
By implementing machine learning algorithms and feeding them data about students (historical performance, attendance, engagement, etc.), educational institutions can build student profiles and then use these profiles to design individual learning pathways for each student. This is what is known as adaptive learning. With this approach, each student will be able to learn at their own pace, which will most likely result in increased academic performance.
Khan Academy is a good example of successful personalized learning implementation. The platform uses machine learning and artificial intelligence to analyze students’ performance in practice exercises and provide personalized content recommendations based on this factor. This allows the company to ensure high student engagement and retention rates while increasing profits.
