One of the ways in which artificial intelligence (AI) is being implemented in the field of education is through Intelligent Tutoring Systems (ITSs). These systems can have a direct impact on student learning, especially when it comes to digital environments. While there has yet to be the large-scale implementation of ITSs in school settings, digital applications have proven to provide students with personalized learning experiences through the use of deep-learning algorithms.
What are ITSs?
Intelligent Tutoring Systems (ITSs) are computer systems that aim to provide personalized instruction and feedback to users, often through the use of AI technology and without a human teacher. ITSs have been receiving a lot of attention due to their ability to provide a one-on-one curriculum. The use of deep learning algorithms allows the systems to suggest certain studying strategies for individuals.
ITSs are becoming more effective with current research, including work coming out of Carnegie Mellon University this year. Researchers were able to demonstrate new methods for building ITSs that can teach a variety of different subjects, including algebra, grammar, equation solving, fraction addition, and chemistry.
Teachers Teaching Computers
One of the newly developed methods involves artificial intelligence allowing a teacher to teach a computer, which then, in turn, teaches a student. In other terms, a computer is being taught how to teach by a human teacher.
In this method, a human teacher demonstrates to the computer how to solve specific problems like multicolumn addition. If the computer provides the wrong solution to the problem, the human teacher then corrects it.
One of the breakthrough aspects of this research and method is that the computer system can solve problems within a topic by generalizing. This means that it does not just solve the problems that it was taught, but it can venture out on its own into new territory.
“A student might learn one way to do a problem and that would be sufficient, but a tutoring system needs to learn every kind of way to solve a problem. It needs to learn how to teach problem-solving, not just how to solve problems,” says Daniel Weitekamp III, a Ph.D. student in CMU’s Human-Computer Interaction Institute (HCII).
One of the more effective methods for developing ITSs resulted in an hour of instruction coming from 40 or 50 hours of development. There has been a shift from humans writing the AI rules for the ITS to the ITS, now writing its own rules.
Machine learning algorithms are utilized to simulate how students learn, and the end goal is for teachers to be able to develop their computerized lessons without having to rely on an AI programmer. If this can be accomplished, ITSs will take a significant leap toward being implemented throughout the education sector, online and in classrooms. Teachers would be able to shape the technology around their personal views and techniques for teaching, highlighting the personalization aspect that makes this technology so valuable, from teacher to student.
History of ITSs
The origin of ITSs can be traced back to the 1960s and ‘70s with the development of Intelligent Computer-Assisted Learning (ICAI) in 1970. Twelve years later in 1982, the term “Intelligent Tutoring Systems” became more popular. All of the ideas within the field came to a point when the first ITS conference was held in 1988, which resulted in several research labs embarking on the path to developing these systems. Since then, there has been a constant improvement in the efficiency of these systems and an increased implementation within the sector.
The Future of Education
ITSs have the opportunity to play a major role in the future of education, solving many of the problems that are present in the sector today. One of the greatest challenges surrounding the education of young individuals, and applicable to any individual regardless of age, is that humans are complicated and require personalized methods of learning to excel. This runs counter to what many of our current education systems are based around – standardized testing and a one-size-fits-all policy. This current model has resulted in individuals being left behind, and it fails to highlight specific skill sets within learners.
AI technology and systems like ITSs address this challenge head-on, creating an environment of learning that revolves around personalized curriculum and highlighting individual skills and interests. Experts widely hold this as the most effective method of teaching, and many nations are moving towards it. In the near future, learning environments, either in a classroom or digital, will undoubtedly consist of transformative new tools based on AI technology like ITSs.
At ROYBI, we like to remind ourselves every day that the future is now, and we need to focus ahead on what can improve our education system! To build our smart robot, we knew the appropriate content and functional software can only work in hardware that is suitable for young children. That is why our first step on the drawing board was to create a robot that fits its young users’ hands. We wanted children to fill close to an intelligent system that teaches them STEAM education and can also be a companion. As a small book that a child can hold near to her to immerse herself in the shapes, pictures, and words of each page, Roybi Robot is also small, light, and portable. Not adding facial features to our robot was also an intentional choice, because we don’t want to create an illusion that a robot is like a human. We hope to tell you more about the journey of our robot’s creation and the inspirations and research behind it in our future articles. Stay tuned…