Artificial intelligence (AI) is everywhere. Whether it’s your smartphone taking spectacular photos by using smart sensors or Alexa, Siri or Google Assistant having intelligent conversations with you by understanding your intentions, every gadget can be made smarter by AI. However, the impact of AI goes beyond gadgets and can change every aspect of our social interactions.
Not surprisingly, artificial intelligence will also change the way people learn. One of the biggest challenges in education is that everyone learns differently, and AI systems can provide individualized learning for each learner in a way that works best for them.
Artificial intelligence has been around for over 50 years, but it has been a fringe technology until recent years when it started to make sense thanks to data abundance (big data), affordable computing power and advances in machine learning.
In a learning environment, machines collect large amounts of data from the learner, use machine learning algorithms to create meaningful insights (intelligence) about the learner, and then take action to make the learner learn better. Artificial intelligence-driven learning technologies are fundamentally different from those of the past in that they are constantly learning about the learner and making informed decisions.
Let’s go back to a typical school.
The focus of the school and parents is to ensure that students complete the course. Whether it’s science, math or any other subject, the way to excel is to correct your knowledge by constantly checking in. The more you correct, the better the chances of a student getting good grades in school. The only insight parents get at the PTA is the student’s score on the test. Often, teachers and parents don’t know how to get their child to rise to a higher level.
The current system is inadequate in the following three areas.
- No correct data available
- No tools for gaining insight (actionable intelligence) from the data
- No way to take a personalized approach to learning based on insight
The future of education lies in using AI to promote personalization and better learning outcomes for each learner. In an AI-powered cognitive learning environment, the system first learns about the learner through parameters (e.g., the learner’s optimal learning style, proficiency in a topic, level of interest in that topic). A personalized learning path is automatically created based on the insight of the learner to ensure that the learner is able to learn in the best possible way and succeed.
For example, for a video on the same topic, visual learners see it only as a visual illustration, while kinesthetic learners see it more as a “let’s do it together” video.
Likewise, all homework and testing done by the student contributes to the system’s ongoing understanding of the learner. If a student has a word problem in math, the system will be able to determine if the problem is related to the student’s ability to visualize the problem, simplify the problem, or use the correct math formula to solve the problem.
At the same time, there may be gaps in the learning process itself, forcing children to learn by “rote” rather than the right way, and students may not be able to make inferences that prevent them from going beyond what they have written. The other child may not be able to find patterns and relationships and may therefore have difficulty making connections with what they have previously learned. And today, an AI-driven system can capture that data and provide remedies for effective learning.
As learning becomes increasingly paperless, AI-driven learning systems will continue to become more effective and efficient. With AI assistants having intelligent and meaningful conversations, learning will no longer be boring, but will become a child’s game.