A few months ago, one of my colleagues told me that artificial intelligence works by magic.
He was obviously joking. But not too much.
Actually, AI algorithms often work in very mysterious ways and deliver incredible results.
What if we used this “magic” to enhance the educational environment? Would we get the cyber version of Harry Potter?
So, how does AI affect education?
AI-based systems can contribute to education in other interesting ways:
- Personalizing the learning experience
- Making useful previsions about students’ performances
- Helping teachers do their job
- Developing efficient AI tutors and chatbots
- Supporting administrative tasks
- Turning your college into Hogwarts
AI and education today
In the field of education, technology is not just about interactive whiteboards and tablets. In recent years, artificial intelligence has played an increasingly important role in enhancing the teaching experience for both professors and students.
This fruitful relationship between AI and education is already a reality and is set to reshape the entire industry in the near future.
The opportunities for these technologies are so vast that even Microsoft has recently commissioned research on this topic.
The results showed that 99.4% of teachers believe AI will be critical to their school’s competitiveness within the next three years, with 15% calling it a “tipping point”.
In fact, 92% of professionals surveyed said they have started experimenting with AI-based educational tools.
Because it works!
No wonder about the considerable optimism regarding this tech: its potential has already been proven on the field.
For example, according to an independent study by the City University of New York and the University of South Carolina, an average of 34 hours of Duolingo is as effective as an entire college semester of language education.
And guess what? The famous language learning platform makes extensive use of AI and machine learning algorithms.
Not only linguistics
AI goes well beyond language learning!
The education industry has already developed many tech-driven solutions that can offer a wide range of services, such as analyzing the level of knowledge, providing an improvement plan, creating a clone who will study at your place, and so on.
One of them is Third Space Learning. This platform, created by London University scholars, can suggest ways to improve teaching techniques, providing feedback via notifications if the teacher speaks too fast or slow.
AI to teach maths
Another interesting platform has been developed by Carnegie Learning and is focused on mathematics. This system can analyze the users’ actions and allows the tutor to see their progress.
It can also provide more personalized teaching materials, making the learning process more comfortable.
Hey, did I just say “personalized teaching”?
Great, this brings us to the first way in which AI can boost teaching and learning. I’m gonna talk about it in the next chapter!
1. How to personalize education with AI
In recent years, most of the research regarding artificial intelligence has focused on so-called machine learning.
Machine learning (ML) is one of the newer branches of AI. It specializes in creating computer algorithms that can automatically improve their performance through experience.
By “experience”, I mean the analysis and processing of huge amounts of data.
And what should algorithms do with all this information? Maybe sell it to Cambridge Analytica?
Well, no. Rather, ML algorithms are able to recognize specific patterns in the data sets they are provided with, build mathematical models concerning these relations, and use the same models to make predictions or decisions without being explicitly programmed.
What does ML mean for education?
The unique features of ML-based systems, especially their ability to understand data and improve themselves, unlock interesting possibilities in the field of education.
The first one is to allow a more personalized learning experience. In the personalized learning model, students can choose their own learning paths based on what they’re interested in.
Consequently, teachers will fit the student’s curriculum to his interests.
ML and adaptive learning
Another approach supported by ML is adaptive learning, which analyzes a student’s performance in real-time and modifies teaching methods and the curriculum based on that data.
Both of these models require efficient learning analytics, a process of collecting, measuring, and using student data to build their personal profiles and design personalized learning pathways or update the previous ones.
Collected data usually consists of variables such as completion time, video views, test results, number of paper airplanes thrown at classmates, and so on.
A bunch of useful information that will be processed by machine learning algorithms to provide feedback and suggestions to teachers.